The topology of metabolic isotope labeling networks
Directory of Open Access Journals (Sweden)
Wiechert Wolfgang
2007-08-01
Full Text Available Abstract Background Metabolic Flux Analysis (MFA based on isotope labeling experiments (ILEs is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. Results With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Conclusion Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global
Uncovering transcriptional regulation of metabolism by using metabolic network topology
DEFF Research Database (Denmark)
Patil, Kiran Raosaheb; Nielsen, Jens
2005-01-01
therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns...... changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We...... in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic...
Energy Technology Data Exchange (ETDEWEB)
Kalb, Jeffrey L.; Lee, David S.
2008-01-01
Emerging high-bandwidth, low-latency network technology has made network-based architectures both feasible and potentially desirable for use in satellite payload architectures. The selection of network topology is a critical component when developing these multi-node or multi-point architectures. This study examines network topologies and their effect on overall network performance. Numerous topologies were reviewed against a number of performance, reliability, and cost metrics. This document identifies a handful of good network topologies for satellite applications and the metrics used to justify them as such. Since often multiple topologies will meet the requirements of the satellite payload architecture under development, the choice of network topology is not easy, and in the end the choice of topology is influenced by both the design characteristics and requirements of the overall system and the experience of the developer.
Fang, Lei; Yao, Zhijun; An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing
2016-01-01
This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients.
Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.
Directory of Open Access Journals (Sweden)
Sylvain Prigent
2017-01-01
Full Text Available Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from
Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes
DEFF Research Database (Denmark)
Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro
2010-01-01
mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic...
Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.
Zelezniak, Aleksej; Pers, Tune H; Soares, Simão; Patti, Mary Elizabeth; Patil, Kiran Raosaheb
2010-04-01
Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.
Quist, Daniel A [Los Alamos, NM; Gavrilov, Eugene M [Los Alamos, NM; Fisk, Michael E [Jemez, NM
2008-01-15
A method enables the topology of an acyclic fully propagated network to be discovered. A list of switches that comprise the network is formed and the MAC address cache for each one of the switches is determined. For each pair of switches, from the MAC address caches the remaining switches that see the pair of switches are located. For each pair of switches the remaining switches are determined that see one of the pair of switches on a first port and the second one of the pair of switches on a second port. A list of insiders is formed for every pair of switches. It is determined whether the insider for each pair of switches is a graph edge and adjacent ones of the graph edges are determined. A symmetric adjacency matrix is formed from the graph edges to represent the topology of the data link network.
OPTIMAL NETWORK TOPOLOGY DESIGN
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Transportation Network Topologies
Holmes, Bruce J.; Scott, John
2004-01-01
A discomforting reality has materialized on the transportation scene: our existing air and ground infrastructures will not scale to meet our nation's 21st century demands and expectations for mobility, commerce, safety, and security. The consequence of inaction is diminished quality of life and economic opportunity in the 21st century. Clearly, new thinking is required for transportation that can scale to meet to the realities of a networked, knowledge-based economy in which the value of time is a new coin of the realm. This paper proposes a framework, or topology, for thinking about the problem of scalability of the system of networks that comprise the aviation system. This framework highlights the role of integrated communication-navigation-surveillance systems in enabling scalability of future air transportation networks. Scalability, in this vein, is a goal of the recently formed Joint Planning and Development Office for the Next Generation Air Transportation System. New foundations for 21st thinking about air transportation are underpinned by several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems. Complexity science and modern network theory give rise to one of the technological developments of importance. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of scalability, efficiency, robustness, resilience, and other metrics. The paper offers an air transportation system topology as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system architectures, and airspace architectures and procedural concepts. The topology proposed considers air transportation as a system of networks, within which
Topological Fidelity in Sensor Networks
Chintakunta, Harish; Krim, Hamid
2011-01-01
Sensor Networks are inherently complex networks, and many of their associated problems require analysis of some of their global characteristics. These are primarily affected by the topology of the network. We present in this paper, a general framework for a topological analysis of a network, and develop distributed algorithms in a generalized combinatorial setting in order to solve two seemingly unrelated problems, 1) Coverage hole detection and Localization and 2) Worm hole attack detection ...
Optimal Network-Topology Design
Li, Victor O. K.; Yuen, Joseph H.; Hou, Ting-Chao; Lam, Yuen Fung
1987-01-01
Candidate network designs tested for acceptability and cost. Optimal Network Topology Design computer program developed as part of study on topology design and analysis of performance of Space Station Information System (SSIS) network. Uses efficient algorithm to generate candidate network designs consisting of subsets of set of all network components, in increasing order of total costs and checks each design to see whether it forms acceptable network. Technique gives true cost-optimal network and particularly useful when network has many constraints and not too many components. Program written in PASCAL.
Transportation Network Topologies
Alexandrov, Natalia (Editor)
2004-01-01
The existing U.S. hub-and-spoke air transportation system is reaching saturation. Major aspects of the current system, such as capacity, safety, mobility, customer satisfaction, security, communications, and ecological effects, require improvements. The changing dynamics - increased presence of general aviation, unmanned autonomous vehicles, military aircraft in civil airspace as part of homeland defense - contributes to growing complexity of airspace. The system has proven remarkably resistant to change. NASA Langley Research Center and the National Institute of Aerospace conducted a workshop on Transportation Network Topologies on 9-10 December 2003 in Williamsburg, Virginia. The workshop aimed to examine the feasibility of traditional methods for complex system analysis and design as well as potential novel alternatives in application to transportation systems, identify state-of-the-art models and methods, conduct gap analysis, and thus to lay a foundation for establishing a focused research program in complex systems applied to air transportation.
Topological Rankings in Communication Networks
DEFF Research Database (Denmark)
Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Træholt, Chresten
2015-01-01
In the theory of communication the central problem is to study how agents exchange information. This problem may be studied using the theory of connected spaces in topology, since a communication network can be modelled as a topological space such that agents can communicate if and only...... if they belong to the same path connected component of that space. In order to study combinatorial properties of such a communication network, notions from algebraic topology are applied. This makes it possible to determine the shape of a network by concrete invariants, e.g. the number of connected components...
Coverings, Networks and Weak Topologies
Czech Academy of Sciences Publication Activity Database
Dow, A.; Junnila, H.; Pelant, Jan
2006-01-01
Roč. 53, č. 2 (2006), s. 287-320 ISSN 0025-5793 R&D Projects: GA ČR GA201/97/0216 Institutional research plan: CEZ:AV0Z10190503 Keywords : Banach spaces * weak topologies * networks topologies Subject RIV: BA - General Mathematics
Network topology descriptions in hybrid networks
Grosso, P.; Brown, A.; Cedeyn, A.; Dijkstra, F.; van der Ham, J.; Patil, A.; Primet, P.; Swany, M.; Zurawski, J.
2010-01-01
The NML-WG goal is to define a schema for describing topologies of hybrid networks. This schema is in first instance intended for: • lightpath provisioning applications to exchange topology information intra and inter domain; • reporting performance metrics. This document constitutes Deliverable 1
Topology of molecular interaction networks
Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; De Ridder, D.
2013-01-01
Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over
Topological analysis of telecommunications networks
Directory of Open Access Journals (Sweden)
Milojko V. Jevtović
2011-01-01
Full Text Available A topological analysis of the structure of telecommunications networks is a very interesting topic in the network research, but also a key issue in their design and planning. Satisfying multiple criteria in terms of locations of switching nodes as well as their connectivity with respect to the requests for capacity, transmission speed, reliability, availability and cost are the main research objectives. There are three ways of presenting the topology of telecommunications networks: table, matrix or graph method. The table method is suitable for a network of a relatively small number of nodes in relation to the number of links. The matrix method involves the formation of a connection matrix in which its columns present source traffic nodes and its rows are the switching systems that belong to the destination. The method of the topology graph means that the network nodes are connected via directional or unidirectional links. We can thus easily analyze the structural parameters of telecommunications networks. This paper presents the mathematical analysis of the star-, ring-, fully connected loop- and grid (matrix-shaped topology as well as the topology based on the shortest path tree. For each of these topologies, the expressions for determining the number of branches, the middle level of reliability, the medium length and the average length of the link are given in tables. For the fully connected loop network with five nodes the values of all topological parameters are calculated. Based on the topological parameters, the relationships that represent integral and distributed indicators of reliability are given in this work as well as the values of the particular network. The main objectives of the topology optimization of telecommunications networks are: achieving the minimum complexity, maximum capacity, the shortest path message transfer, the maximum speed of communication and maximum economy. The performance of telecommunications networks is
Limitations of Passively Mapping Logical Network Topologies
Ayodeji J. Akande; Colin Fidge; Ernest Foo
2017-01-01
Understanding logical network connectivity is essential in network topology mapping especially in a fast growing network where knowing what is happening on the network is critical for security purposes and where knowing how network resources are being used is highly important. Mapping logical communication topology is important for network auditing, network maintenance and governance, network optimization, and network security. However, the process of capturing network traffic to generate the...
Topological Indices of Textual Identity Networks.
Leazer, Gregory H.; Furner, Jonathan
1999-01-01
Reports on a continuing investigation of intertextual networks. Describes how intertextual networks can be modeled as directed graphs and extends this to matrix representations. Discusses topological index values of these networks and speculates how topological index values might be used in the estimation of retrieval values in information…
How to model wireless mesh networks topology
International Nuclear Information System (INIS)
Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M
2013-01-01
The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches
Entangled Networks, Synchronization, and Optimal Network Topology
Donetti, Luca; Hurtado, Pablo I.; Muñoz, Miguel A.
2005-10-01
A new family of graphs, entangled networks, with optimal properties in many respects, is introduced. By definition, their topology is such that it optimizes synchronizability for many dynamical processes. These networks are shown to have an extremely homogeneous structure: degree, node distance, betweenness, and loop distributions are all very narrow. Also, they are characterized by a very interwoven (entangled) structure with short average distances, large loops, and no well-defined community structure. This family of nets exhibits an excellent performance with respect to other flow properties such as robustness against errors and attacks, minimal first-passage time of random walks, efficient communication, etc. These remarkable features convert entangled networks in a useful concept, optimal or almost optimal in many senses, and with plenty of potential applications in computer science or neuroscience.
DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.
Energy Technology Data Exchange (ETDEWEB)
MASLOV,S.SNEPPEN,K.
2003-11-17
interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which
Symmetric Topological Phases and Tensor Network States
Jiang, Shenghan
Classification and simulation of quantum phases are one of main themes in condensed matter physics. Quantum phases can be distinguished by their symmetrical and topological properties. The interplay between symmetry and topology in condensed matter physics often leads to exotic quantum phases and rich phase diagrams. Famous examples include quantum Hall phases, spin liquids and topological insulators. In this thesis, I present our works toward a more systematically understanding of symmetric topological quantum phases in bosonic systems. In the absence of global symmetries, gapped quantum phases are characterized by topological orders. Topological orders in 2+1D are well studied, while a systematically understanding of topological orders in 3+1D is still lacking. By studying a family of exact solvable models, we find at least some topological orders in 3+1D can be distinguished by braiding phases of loop excitations. In the presence of both global symmetries and topological orders, the interplay between them leads to new phases termed as symmetry enriched topological (SET) phases. We develop a framework to classify a large class of SET phases using tensor networks. For each tensor class, we can write down generic variational wavefunctions. We apply our method to study gapped spin liquids on the kagome lattice, which can be viewed as SET phases of on-site symmetries as well as lattice symmetries. In the absence of topological order, symmetry could protect different topological phases, which are often referred to as symmetry protected topological (SPT) phases. We present systematic constructions of tensor network wavefunctions for bosonic symmetry protected topological (SPT) phases respecting both onsite and spatial symmetries.
Synchronization in complex networks with switching topology
International Nuclear Information System (INIS)
Wang, Lei; Wang, Qing-guo
2011-01-01
This Letter investigates synchronization issues of complex dynamical networks with switching topology. By constructing a common Lyapunov function, we show that local and global synchronization for a linearly coupled network with switching topology can be evaluated by the time average of second smallest eigenvalues corresponding to the Laplacians of switching topology. This result is quite powerful and can be further used to explore various switching cases for complex dynamical networks. Numerical simulations illustrate the effectiveness of the obtained results in the end. -- Highlights: → Synchronization of complex networks with switching topology is investigated. → A common Lyapunov function is established for synchronization of switching network. → The common Lyapunov function is not necessary to monotonically decrease with time. → Synchronization is determined by the second smallest eigenvalue of its Laplacian. → Synchronization criterion can be used to investigate various switching cases.
Machine Learning Topological Invariants with Neural Networks
Zhang, Pengfei; Shen, Huitao; Zhai, Hui
2018-02-01
In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.
Spectrum-Based and Collaborative Network Topology Analysis and Visualization
Hu, Xianlin
2013-01-01
Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…
Litinski, Daniel; Kesselring, Markus S.; Eisert, Jens; von Oppen, Felix
2017-07-01
We present a scalable architecture for fault-tolerant topological quantum computation using networks of voltage-controlled Majorana Cooper pair boxes and topological color codes for error correction. Color codes have a set of transversal gates which coincides with the set of topologically protected gates in Majorana-based systems, namely, the Clifford gates. In this way, we establish color codes as providing a natural setting in which advantages offered by topological hardware can be combined with those arising from topological error-correcting software for full-fledged fault-tolerant quantum computing. We provide a complete description of our architecture, including the underlying physical ingredients. We start by showing that in topological superconductor networks, hexagonal cells can be employed to serve as physical qubits for universal quantum computation, and we present protocols for realizing topologically protected Clifford gates. These hexagonal-cell qubits allow for a direct implementation of open-boundary color codes with ancilla-free syndrome read-out and logical T gates via magic-state distillation. For concreteness, we describe how the necessary operations can be implemented using networks of Majorana Cooper pair boxes, and we give a feasibility estimate for error correction in this architecture. Our approach is motivated by nanowire-based networks of topological superconductors, but it could also be realized in alternative settings such as quantum-Hall-superconductor hybrids.
Scaling in topological properties of brain networks
Singh, S.S.; Khundrakpam, B.S.; Reid, A.T.; Lewis, J.D.; Evans, A.C.; Ishrat, R.; Sharma, B.I.; Singh, R.K.B.
2016-01-01
The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks.
Distribution network topology identification based on synchrophasor
Directory of Open Access Journals (Sweden)
Stefania Conti
2018-03-01
Full Text Available A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.
Inferring network topology from complex dynamics
International Nuclear Information System (INIS)
Shandilya, Srinivas Gorur; Timme, Marc
2011-01-01
Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits.
Topological Analysis of Urban Drainage Networks
Yang, Soohyun; Paik, Kyungrock; McGrath, Gavan; Rao, Suresh
2016-04-01
Urban drainage networks are an essential component of infrastructure, and comprise the aggregation of underground pipe networks carrying storm water and domestic waste water for eventual discharge to natural stream networks. Growing urbanization has contributed to rapid expansion of sewer networks, vastly increasing their complexity and scale. Importance of sewer networks has been well studied from an engineering perspective, including resilient management, optimal design, and malfunctioning impact. Yet, analysis of the urban drainage networks using complex networks approach are lacking. Urban drainage networks consist of manholes and conduits, which correspond to nodes and edges, analogous to junctions and streams in river networks. Converging water flows in these two networks are driven by elevation gradient. In this sense, engineered urban drainage networks share several attributes of flows in river networks. These similarities between the two directed, converging flow networks serve the basis for us to hypothesize that the functional topology of sewer networks, like river networks, is scale-invariant. We analyzed the exceedance probability distribution of upstream area for practical sewer networks in South Korea. We found that the exceedance probability distributions of upstream area follow power-law, implying that the sewer networks exhibit topological self-similarity. The power-law exponents for the sewer networks were similar, and within the range reported from analysis of natural river networks. Thus, in line with our hypothesis, these results suggest that engineered urban drainage networks share functional topological attributes regardless of their structural dissimilarity or different underlying network evolution processes (natural vs. engineered). Implications of these findings for optimal design of sewer networks and for modeling sewer flows will be discussed.
Automated Network Mapping and Topology Verification
2016-06-01
for the scaling of systems and networks while ensuring compatibility between networking entities [2]. While the OSI Model outlines an overall...information to map the topology of a network . 13 Figure 5. OSI Reference Model and the IP Suite Comparison. Source: [10]. a. Address Resolution Protocol...10] T. Pickett. “Back to basics with the OSI model - adventures in network engineering.” [Online]. Available: http://www.tonypickett.com/2013/07
On topological properties of sierpinski networks
International Nuclear Information System (INIS)
Imran, Muhammad; Sabeel-e-Hafi; Gao, Wei; Reza Farahani, Mohammad
2017-01-01
Sierpinski graphs constitute an extensively studied class of graphs of fractal nature applicable in topology, mathematics of Tower of Hanoi, computer science, and elsewhere. A large number of properties like physico-chemical properties, thermodynamic properties, chemical activity, biological activity, etc. are determined by the chemical applications of graph theory. These properties can be characterized by certain graph invariants referred to as topological indices. In QRAR/QSPR study these graph invariants has played a vital role. In this paper, we study the molecular topological properties of Sierpinski networks and derive the analytical closed formulas for the atom-bond connectivity (ABC) index, geometric-arithmetic (GA) index, and fourth and fifth version of these topological indices for Sierpinski networks denoted by S(n, k).
Network-topology-adaptive quantum conference protocols
International Nuclear Information System (INIS)
Zhang Sheng; Wang Jian; Tang Chao-Jing; Zhang Quan
2011-01-01
As an important application of the quantum network communication, quantum multiparty conference has made multiparty secret communication possible. Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology. However, the topology of the quantum network significantly affects the communication efficiency, e.g., parallel transmission in a channel with limited bandwidth. We have proposed two distinctive protocols, which work in two basic network topologies with efficiency higher than the existing ones. We first present a protocol which works in the reticulate network using Greeberger—Horne—Zeilinger states and entanglement swapping. Another protocol, based on quantum multicasting with quantum data compression, which can improve the efficiency of the network, works in the star-like network. The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption. In general, the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols. (general)
Routing in Networks with Random Topologies
Bambos, Nicholas
1997-01-01
We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.
Evolution of metabolic network organization
Directory of Open Access Journals (Sweden)
Bonchev Danail
2010-05-01
Full Text Available Abstract Background Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. Results We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya, from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. Conclusions Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules
Tensor Network Wavefunctions for Topological Phases
Ware, Brayden Alexander
The combination of quantum effects and interactions in quantum many-body systems can result in exotic phases with fundamentally entangled ground state wavefunctions--topological phases. Topological phases come in two types, both of which will be studied in this thesis. In topologically ordered phases, the pattern of entanglement in the ground state wavefunction encodes the statistics of exotic emergent excitations, a universal indicator of a phase that is robust to all types of perturbations. In symmetry protected topological phases, the entanglement instead encodes a universal response of the system to symmetry defects, an indicator that is robust only to perturbations respecting the protecting symmetry. Finding and creating these phases in physical systems is a motivating challenge that tests all aspects--analytical, numerical, and experimental--of our understanding of the quantum many-body problem. Nearly three decades ago, the creation of simple ansatz wavefunctions--such as the Laughlin fractional quantum hall state, the AKLT state, and the resonating valence bond state--spurred analytical understanding of both the role of entanglement in topological physics and physical mechanisms by which it can arise. However, quantitative understanding of the relevant phase diagrams is still challenging. For this purpose, tensor networks provide a toolbox for systematically improving wavefunction ansatz while still capturing the relevant entanglement properties. In this thesis, we use the tools of entanglement and tensor networks to analyze ansatz states for several proposed new phases. In the first part, we study a featureless phase of bosons on the honeycomb lattice and argue that this phase can be topologically protected under any one of several distinct subsets of the crystalline lattice symmetries. We discuss methods of detecting such phases with entanglement and without. In the second part, we consider the problem of constructing fixed-point wavefunctions for
Topology Optimisation of Wireless Sensor Networks
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Thike Aye Min
2016-01-01
Full Text Available Wireless sensor networks are widely used in a variety of fields including industrial environments. In case of a clustered network the location of cluster head affects the reliability of the network operation. Finding of the optimum location of the cluster head, therefore, is critical for the design of a network. This paper discusses the optimisation approach, based on the brute force algorithm, in the context of topology optimisation of a cluster structure centralised wireless sensor network. Two examples are given to verify the approach that demonstrate the implementation of the brute force algorithm to find an optimum location of the cluster head.
Optimal topologies for maximizing network transmission capacity
Chen, Zhenhao; Wu, Jiajing; Rong, Zhihai; Tse, Chi K.
2018-04-01
It has been widely demonstrated that the structure of a network is a major factor that affects its traffic dynamics. In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery. We propose an approach for designing optimal network topologies against traffic congestion by link rewiring and apply them on the Barabási-Albert scale-free, static scale-free and Internet Autonomous System-level networks. Furthermore, we analyze the optimized networks using complex network parameters that characterize the structure of networks, and our simulation results suggest that an optimal network for traffic transmission is more likely to have a core-periphery structure. However, assortative mixing and the rich-club phenomenon may have negative impacts on network performance. Based on the observations of the optimized networks, we propose an efficient method to improve the transmission capacity of large-scale networks.
DEALING WITH TOPOLOGICAL RELATIONS IN UNDERGROUND NETWORKS
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M. Lacroix
2015-01-01
Full Text Available Ten years ago, 25 people died and more than 150 were seriously injured in Ghislenghien (Belgium because of construction damage to a high pressure gas pipeline. Urban networks are invisible because usually buried between 1 and 1.5 meter underground. They should be identified to prevent such accidents which involve workers and public as well. Rural and urban districts, networks concessionary and contractors; everyone could benefit from their networks becoming safer. To develop software which evaluates the risks in managing both uncertainties and topology is the focus of attention. That’s the reason why we firstly propose to determine the topological relationships between networks; secondly we propose to compute the risks taking into account the various uncertainties such as the security radius or the coordinates accuracy, before giving the different required standards for an artificial intelligence tool in order to obtain high security level.
Dynamical networks with topological self-organization
Zak, M.
2001-01-01
Coupled evolution of state and topology of dynamical networks is introduced. Due to the well organized tensor structure, the governing equations are presented in a canonical form, and required attractors as well as their basins can be easily implanted and controlled.
Spectral Analysis of Rich Network Topology in Social Networks
Wu, Leting
2013-01-01
Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…
Scheduling and Topology Design in Networks with Directional Antennas
2017-05-19
Scheduling and Topology Design in Networks with Directional Antennas Thomas Stahlbuhk, Nathaniel M. Jones, Brooke Shrader Lincoln Laboratory...controllers must choose which pairs of nodes should communicate in order to establish a topology over which traffic can be sent. Additionally...interacting effects of topology design and transmission scheduling in wireless networks, in particular focusing on networks where nodes are divided into
Topology Control in Aerial Multi-Beam Directional Networks
2017-04-24
Topology Control in Aerial Multi-Beam Directional Networks Brian Proulx, Nathaniel M. Jones, Jennifer Madiedo, Greg Kuperman {brian.proulx, njones...significant interference. Topology control (i.e., selecting a subset of neighbors to communicate with) is vital to reduce the interference. Good topology ...underlying challenges to topology control in multi-beam direction networks. Two topology control algorithms are developed: a centralized algorithm
Structural correlations in bacterial metabolic networks
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Lizana Ludvig
2011-01-01
Full Text Available Abstract Background Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. Results We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD, a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. Conclusions The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery
Structural correlations in bacterial metabolic networks.
Bernhardsson, Sebastian; Gerlee, Philip; Lizana, Ludvig
2011-01-20
Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests that natural selection and
Topology evolution in macromolecular networks
Kryven, I.
2014-01-01
Governed by various intermolecular forces, molecular networks tend to evolve from simple to very complex formations that have random structure. This randomness in the connectivity of the basic units can still be captured employing distributional description of the state of the system; the evolution
Systemic risk on different interbank network topologies
Lenzu, Simone; Tedeschi, Gabriele
2012-09-01
In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents' performance. By changing the agent's trust on its neighbor's performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents' heterogeneity.
On the topology of optical transport networks
International Nuclear Information System (INIS)
Cardenas, J P; Santiago, A; Losada, J C; Benito, R M; Mouronte, M L
2010-01-01
Synchronous Digital Hierarchy (SDH) is the standard technology for information transmission in broadband optical networks. Unlike systems with unplanned growth, such as those of natural origin or the Internet network, the SDH systems are strictly planned as rings, meshes, stars or tree-branches structures designed to connect different equipments. In spite of that, we have found that the SDH network operated by Telefonica in Spain shares remarkable topological properties with other real complex networks as a product of its evolution since 1992. In fact, we have found power-law scaling in the degree distribution (N·P(k) = k -γ ) and small-world networks properties. The complexity found in SDH systems was reproduced by two models of complex networks, one of them considers real planning directives that take into account geographical and technological variables and the other one is based in the compatibility among SDH equipments.
Topology control with IPD network creation games
International Nuclear Information System (INIS)
Scholz, Jan C; Greiner, Martin O W
2007-01-01
Network creation games couple a two-players game with the evolution of network structure. A vertex player may increase its own payoff with a change of strategy or with a modification of its edge-defined neighbourhood. By referring to the iterated prisoners dilemma (IPD) game we show that this evolutionary dynamics converges to network-Nash equilibria, where no vertex is able to improve its payoff. The resulting network structure exhibits a strong dependence on the parameter of the payoff matrix. Degree distributions and cluster coefficients are also strongly affected by the specific interactions chosen for the neighbourhood exploration. This allows network creation games to be seen as a promising artificial-social-systems approach for a distributive topology control of complex networked systems
Topology control with IPD network creation games
Energy Technology Data Exchange (ETDEWEB)
Scholz, Jan C [Frankfurt Institute for Advanced Studies and Frankfurt International Graduate School for Science, Johann Wolfgang Goethe Universitaet, Max-von-Laue-Strasse 1, D-60438 Frankfurt (Germany); Greiner, Martin O W [Corporate Technology, Information and Communications, Siemens AG, D-81730 Munich (Germany)
2007-06-15
Network creation games couple a two-players game with the evolution of network structure. A vertex player may increase its own payoff with a change of strategy or with a modification of its edge-defined neighbourhood. By referring to the iterated prisoners dilemma (IPD) game we show that this evolutionary dynamics converges to network-Nash equilibria, where no vertex is able to improve its payoff. The resulting network structure exhibits a strong dependence on the parameter of the payoff matrix. Degree distributions and cluster coefficients are also strongly affected by the specific interactions chosen for the neighbourhood exploration. This allows network creation games to be seen as a promising artificial-social-systems approach for a distributive topology control of complex networked systems.
Fermionic topological quantum states as tensor networks
Wille, C.; Buerschaper, O.; Eisert, J.
2017-06-01
Tensor network states, and in particular projected entangled pair states, play an important role in the description of strongly correlated quantum lattice systems. They do not only serve as variational states in numerical simulation methods, but also provide a framework for classifying phases of quantum matter and capture notions of topological order in a stringent and rigorous language. The rapid development in this field for spin models and bosonic systems has not yet been mirrored by an analogous development for fermionic models. In this work, we introduce a tensor network formalism capable of capturing notions of topological order for quantum systems with fermionic components. At the heart of the formalism are axioms of fermionic matrix-product operator injectivity, stable under concatenation. Building upon that, we formulate a Grassmann number tensor network ansatz for the ground state of fermionic twisted quantum double models. A specific focus is put on the paradigmatic example of the fermionic toric code. This work shows that the program of describing topologically ordered systems using tensor networks carries over to fermionic models.
Characterizing granular networks using topological metrics
Dijksman, Joshua A.; Kovalcinova, Lenka; Ren, Jie; Behringer, Robert P.; Kramar, Miroslav; Mischaikow, Konstantin; Kondic, Lou
2018-04-01
We carry out a direct comparison of experimental and numerical realizations of the exact same granular system as it undergoes shear jamming. We adjust the numerical methods used to optimally represent the experimental settings and outcomes up to microscopic contact force dynamics. Measures presented here range from microscopic through mesoscopic to systemwide characteristics of the system. Topological properties of the mesoscopic force networks provide a key link between microscales and macroscales. We report two main findings: (1) The number of particles in the packing that have at least two contacts is a good predictor for the mechanical state of the system, regardless of strain history and packing density. All measures explored in both experiments and numerics, including stress-tensor-derived measures and contact numbers depend in a universal manner on the fraction of nonrattler particles, fNR. (2) The force network topology also tends to show this universality, yet the shape of the master curve depends much more on the details of the numerical simulations. In particular we show that adding force noise to the numerical data set can significantly alter the topological features in the data. We conclude that both fNR and topological metrics are useful measures to consider when quantifying the state of a granular system.
Topology Design for Directional Range Extension Networks with Antenna Blockage
2017-03-19
Topology Design for Directional Range Extension Networks with Antenna Blockage Thomas Shake MIT Lincoln Laboratory shake@ll.mit.edu Abstract...associated electronics into small aircraft to perform such range extension. In particular, the paper examines trade-offs in network topology design...aircraft, and the topology characteristics of the aerial relay network. The analysis suggests that low-degree air topologies such as rings and strings
VRML metabolic network visualizer.
Rojdestvenski, Igor
2003-03-01
A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.
Optimization-based topology identification of complex networks
International Nuclear Information System (INIS)
Tang Sheng-Xue; Chen Li; He Yi-Gang
2011-01-01
In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)
Novel topological descriptors for analyzing biological networks
Directory of Open Access Journals (Sweden)
Varmuza Kurt K
2010-06-01
Full Text Available Abstract Background Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. Results In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. Conclusions Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.
Block information and topology in memory networks
Dominguez, David
2007-02-01
The retrieval abilities of spatially uniform attractor networks can be measured by the average overlap between patterns and neural states. Metric networks (with local connections), like small-world graphs, modelled by the parameters: connectivity γ and randomness ω, however, display a richer distribution of memory attractors. We found that metric networks can carry information structured in blocks without any global overlap. There is a competition between global and blocks attractors. We propose a way to measure the block information, related to the fluctuations of the overlap over the blocks. The phase-diagram with the transition from local to global information, shows that the stability of blocks grows with dilution, but decreases with the storage rate and disappears for random topologies.
Complex brain networks: From topological communities to clustered ...
Indian Academy of Sciences (India)
Complex brain networks: From topological communities to clustered dynamics ... http://www.ias.ac.in/article/fulltext/pram/070/06/1087-1097 ... We find that this network of networks displays clustered synchronisation behaviour and the dynamical clusters closely coincide with the topological community structures observed in ...
Mechanized Extraction of Topology Anti-patterns in Wireless Networks
Woehrle, M.; Bakhshi, R.R.; Mousavi, M.R.
2012-01-01
Exhaustive and mechanized formal verification of wireless networks is hampered by the huge number of possible topologies and the large size of the actual networks. However, the generic communication structure in such networks allows for reducing the root causes of faults to faulty (sub-)topologies,
Shuffle-Exchange Mesh Topology for Networks-on-Chip
Sabbaghi-Nadooshan, Reza; Modarressi, Mehdi; Sarbazi-Azad, Hamid
2010-01-01
The mesh topology has been used in a variety of interconnection network applications especially for NoC designs due to its desirable properties in VLSI implementation. In this chapter, we proposed a new topology based on the shuffle-exchange topology, the 2D
Functional Topology of Evolving Urban Drainage Networks
Yang, Soohyun; Paik, Kyungrock; McGrath, Gavan S.; Urich, Christian; Krueger, Elisabeth; Kumar, Praveen; Rao, P. Suresh C.
2017-11-01
We investigated the scaling and topology of engineered urban drainage networks (UDNs) in two cities, and further examined UDN evolution over decades. UDN scaling was analyzed using two power law scaling characteristics widely employed for river networks: (1) Hack's law of length (L)-area (A) [L∝Ah] and (2) exceedance probability distribution of upstream contributing area (δ) [P>(A≥δ>)˜aδ-ɛ]. For the smallest UDNs ((A≥δ>) plots for river networks are abruptly truncated, those for UDNs display exponential tempering [P>(A≥δ>)=aδ-ɛexp>(-cδ>)]. The tempering parameter c decreases as the UDNs grow, implying that the distribution evolves in time to resemble those for river networks. However, the power law exponent ɛ for large UDNs tends to be greater than the range reported for river networks. Differences in generative processes and engineering design constraints contribute to observed differences in the evolution of UDNs and river networks, including subnet heterogeneity and nonrandom branching.
A network perspective on metabolic inconsistency
Directory of Open Access Journals (Sweden)
Sonnenschein Nikolaus
2012-05-01
Full Text Available Abstract Background Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. Results Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. Conclusions The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical
Topological evolution of virtual social networks by modeling social activities
Sun, Xin; Dong, Junyu; Tang, Ruichun; Xu, Mantao; Qi, Lin; Cai, Yang
2015-09-01
With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays' social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks.
Inferring epidemic network topology from surveillance data.
Directory of Open Access Journals (Sweden)
Xiang Wan
Full Text Available The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.
Topology Identification of General Dynamical Network with Distributed Time Delays
International Nuclear Information System (INIS)
Zhao-Yan, Wu; Xin-Chu, Fu
2009-01-01
General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)
Identification of Boolean Networks Using Premined Network Topology Information.
Zhang, Xiaohua; Han, Huaxiang; Zhang, Weidong
2017-02-01
This brief aims to reduce the data requirement for the identification of Boolean networks (BNs) by using the premined network topology information. First, a matching table is created and used for sifting the true from the false dependences among the nodes in the BNs. Then, a dynamic extension to matching table is developed to enable the dynamic locating of matching pairs to start as soon as possible. Next, based on the pseudocommutative property of the semitensor product, a position-transform mining is carried out to further improve data utilization. Combining the above, the topology of the BNs can be premined for the subsequent identification. Examples are given to illustrate the efficiency of reducing the data requirement. Some excellent features, such as the online and parallel processing ability, are also demonstrated.
The topology and dynamics of complex networks
Dezso, Zoltan
We start with a brief introduction about the topological properties of real networks. Most real networks are scale-free, being characterized by a power-law degree distribution. The scale-free nature of real networks leads to unexpected properties such as the vanishing epidemic threshold. Traditional methods aiming to reduce the spreading rate of viruses cannot succeed on eradicating the epidemic on a scale-free network. We demonstrate that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate the virus. We find that the more biased a policy is towards the hubs, the more chance it has to bring the epidemic threshold above the virus' spreading rate. We continue by studying a large Web portal as a model system for a rapidly evolving network. We find that the visitation pattern of a news document decays as a power law, in contrast with the exponential prediction provided by simple models of site visitation. This is rooted in the inhomogeneous nature of the browsing pattern characterizing individual users: the time interval between consecutive visits by the same user to the site follows a power law distribution, in contrast with the exponential expected for Poisson processes. We show that the exponent characterizing the individual user's browsing patterns determines the power-law decay in a document's visitation. Finally, we turn our attention to biological networks and demonstrate quantitatively that protein complexes in the yeast, Saccharomyces cerevisiae, are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or non-essential) and share identical functional classification and cellular localization. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.
Hypergraph topological quantities for tagged social networks
Zlatić, Vinko; Ghoshal, Gourab; Caldarelli, Guido
2009-09-01
Recent years have witnessed the emergence of a new class of social networks, which require us to move beyond previously employed representations of complex graph structures. A notable example is that of the folksonomy, an online process where users collaboratively employ tags to resources to impart structure to an otherwise undifferentiated database. In a recent paper, we proposed a mathematical model that represents these structures as tripartite hypergraphs and defined basic topological quantities of interest. In this paper, we extend our model by defining additional quantities such as edge distributions, vertex similarity and correlations as well as clustering. We then empirically measure these quantities on two real life folksonomies, the popular online photo sharing site Flickr and the bookmarking site CiteULike. We find that these systems share similar qualitative features with the majority of complex networks that have been previously studied. We propose that the quantities and methodology described here can be used as a standard tool in measuring the structure of tagged networks.
Network topology of an experimental futures exchange
Wang, S. C.; Tseng, J. J.; Tai, C. C.; Lai, K. H.; Wu, W. S.; Chen, S. H.; Li, S. P.
2008-03-01
Many systems of different nature exhibit scale free behaviors. Economic systems with power law distribution in the wealth are one of the examples. To better understand the working behind the complexity, we undertook an experiment recording the interactions between market participants. A Web server was setup to administer the exchange of futures contracts whose liquidation prices were coupled to event outcomes. After free registration, participants started trading to compete for the money prizes upon maturity of the futures contracts at the end of the experiment. The evolving `cash' flow network was reconstructed from the transactions between players. We show that the network topology is hierarchical, disassortative and small-world with a power law exponent of 1.02±0.09 in the degree distribution after an exponential decay correction. The small-world property emerged early in the experiment while the number of participants was still small. We also show power law-like distributions of the net incomes and inter-transaction time intervals. Big winners and losers are associated with high degree, high betweenness centrality, low clustering coefficient and low degree-correlation. We identify communities in the network as groups of the like-minded. The distribution of the community sizes is shown to be power-law distributed with an exponent of 1.19±0.16.
THE EFFECT OF TOPOLOGY ON TEMPORAL NETWORK DYNAMICS
Directory of Open Access Journals (Sweden)
Valentina Yu. Guleva
2016-11-01
Full Text Available The effect of initial network topology on a temporal network dynamics is studied. An example of interbank exposures network is considered. It is modeled with a graph, where banks are represented by nodes and interbank lending is represented by edges. The dynamical processes in аtemporal network are defined by state changes of nodes and lie in edges and nodes addition and deletion in a graph, and modification of node states contribute to network evolution. The algorithm of network modification over the whole evolution period is fixed. We present parameters of random, scale free and small world generative models corresponding to different simulation results with fixed modification algorithms. The influence of initial graph topologies on temporal network dynamics is demonstrated. The results obtained give the possibility to assess time interval before the attainment of unstable topology state, and to estimate an optimal topology for the transition to a steady state under fixed modification algorithms.
Robust quantum network architectures and topologies for entanglement distribution
Das, Siddhartha; Khatri, Sumeet; Dowling, Jonathan P.
2018-01-01
Entanglement distribution is a prerequisite for several important quantum information processing and computing tasks, such as quantum teleportation, quantum key distribution, and distributed quantum computing. In this work, we focus on two-dimensional quantum networks based on optical quantum technologies using dual-rail photonic qubits for the building of a fail-safe quantum internet. We lay out a quantum network architecture for entanglement distribution between distant parties using a Bravais lattice topology, with the technological constraint that quantum repeaters equipped with quantum memories are not easily accessible. We provide a robust protocol for simultaneous entanglement distribution between two distant groups of parties on this network. We also discuss a memory-based quantum network architecture that can be implemented on networks with an arbitrary topology. We examine networks with bow-tie lattice and Archimedean lattice topologies and use percolation theory to quantify the robustness of the networks. In particular, we provide figures of merit on the loss parameter of the optical medium that depend only on the topology of the network and quantify the robustness of the network against intermittent photon loss and intermittent failure of nodes. These figures of merit can be used to compare the robustness of different network topologies in order to determine the best topology in a given real-world scenario, which is critical in the realization of the quantum internet.
Robustness of metabolic networks
Jeong, Hawoong
2009-03-01
We investigated the robustness of cellular metabolism by simulating the system-level computational models, and also performed the corresponding experiments to validate our predictions. We address the cellular robustness from the ``metabolite''-framework by using the novel concept of ``flux-sum,'' which is the sum of all incoming or outgoing fluxes (they are the same under the pseudo-steady state assumption). By estimating the changes of the flux-sum under various genetic and environmental perturbations, we were able to clearly decipher the metabolic robustness; the flux-sum around an essential metabolite does not change much under various perturbations. We also identified the list of the metabolites essential to cell survival, and then ``acclimator'' metabolites that can control the cell growth were discovered. Furthermore, this concept of ``metabolite essentiality'' should be useful in developing new metabolic engineering strategies for improved production of various bioproducts and designing new drugs that can fight against multi-antibiotic resistant superbacteria by knocking-down the enzyme activities around an essential metabolite. Finally, we combined a regulatory network with the metabolic network to investigate its effect on dynamic properties of cellular metabolism.
Molecular network topology and reliability for multipurpose diagnosis
Jalil, MA; Moongfangklang, N; Innate, K; Mitatha, S; Ali, J; Yupapin, PP
2011-01-01
This investigation proposes the use of molecular network topology for drug delivery and diagnosis network design. Three modules of molecular network topologies, such as bus, star, and ring networks, are designed and manipulated based on a micro- and nanoring resonator system. The transportation of the trapping molecules by light in the network is described and the theoretical background is reviewed. The quality of the network is analyzed and calculated in terms of signal transmission (ie, signal to noise ratio and crosstalk effects). Results obtained show that a bus network has advantages over star and ring networks, where the use of mesh networks is possible. In application, a thin film network can be fabricated in the form of a waveguide and embedded in artificial bone, which can be connected to the required drug targets. The particular drug/nutrient can be transported to the required targets via the particular network used. PMID:22072875
Molecular network topology and reliability for multipurpose diagnosis.
Moongfangklang, N; Jalil, M A; Innate, K; Mitatha, S; Ali, J; Yupapin, P P
2011-01-01
This investigation proposes the use of molecular network topology for drug delivery and diagnosis network design. Three modules of molecular network topologies, such as bus, star, and ring networks, are designed and manipulated based on a micro- and nanoring resonator system. The transportation of the trapping molecules by light in the network is described and the theoretical background is reviewed. The quality of the network is analyzed and calculated in terms of signal transmission (ie, signal to noise ratio and crosstalk effects). Results obtained show that a bus network has advantages over star and ring networks, where the use of mesh networks is possible. In application, a thin film network can be fabricated in the form of a waveguide and embedded in artificial bone, which can be connected to the required drug targets. The particular drug/nutrient can be transported to the required targets via the particular network used.
Characterization of Static/Dynamic Topological Routing For Grid Networks
DEFF Research Database (Denmark)
Gutierrez Lopez, Jose Manuel; Cuevas, Ruben; Riaz, M. Tahir
2009-01-01
Grid or 2D Mesh structures are becoming one of the most attractive network topologies to study. They can be used in many different fields raging from future broadband networks to multiprocessors structures. In addition, the high requirements of future services and applications demand more flexible...... and adaptive networks. Topological routing in grid networks is a simple and efficient alternative to traditional routing techniques, e.g. routing tables, and the paper extends this kind of routing providing a "Dynamic" attribute. This new property attempts to improve the overall network performance for future...
SIMULATION OF WIRELESS SENSOR NETWORK WITH HYBRID TOPOLOGY
Directory of Open Access Journals (Sweden)
J. Jaslin Deva Gifty
2016-03-01
Full Text Available The design of low rate Wireless Personal Area Network (WPAN by IEEE 802.15.4 standard has been developed to support lower data rates and low power consuming application. Zigbee Wireless Sensor Network (WSN works on the network and application layer in IEEE 802.15.4. Zigbee network can be configured in star, tree or mesh topology. The performance varies from topology to topology. The performance parameters such as network lifetime, energy consumption, throughput, delay in data delivery and sensor field coverage area varies depending on the network topology. In this paper, designing of hybrid topology by using two possible combinations such as star-tree and star-mesh is simulated to verify the communication reliability. This approach is to combine all the benefits of two network model. The parameters such as jitter, delay and throughput are measured for these scenarios. Further, MAC parameters impact such as beacon order (BO and super frame order (SO for low power consumption and high channel utilization, has been analysed for star, tree and mesh topology in beacon disable mode and beacon enable mode by varying CBR traffic loads.
Phylogeny of metabolic networks: A spectral graph theoretical ...
Indian Academy of Sciences (India)
The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a ...
ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies
National Research Council Canada - National Science Library
Matteucci, Matteo
2006-01-01
In this paper we present ELeaRNT an evolutionary strategy which evolves rich neural network topologies in order to find an optimal domain specific non linear function approximator with a good generalization performance...
Tight Network Topology Dependent Bounds on Rounds of Communication
Chattopadhyay, Arkadev; Langberg, Michael; Li, Shi; Rudra, Atri
2016-01-01
We prove tight network topology dependent bounds on the round complexity of computing well studied $k$-party functions such as set disjointness and element distinctness. Unlike the usual case in the CONGEST model in distributed computing, we fix the function and then vary the underlying network topology. This complements the recent such results on total communication that have received some attention. We also present some applications to distributed graph computation problems. Our main contri...
Energy efficient topology control algorithm for wireless mesh networks
CSIR Research Space (South Africa)
Aron, FO
2008-08-01
Full Text Available The control of the topology of a network makes it possible for the network nodes to reduce their power of transmission while ensuring that network connectivity is preserved. This paper explains the need for energy consumption control in Wireless...
Throughput Analysis of Large Wireless Networks with Regular Topologies
Directory of Open Access Journals (Sweden)
Hong Kezhu
2007-01-01
Full Text Available The throughput of large wireless networks with regular topologies is analyzed under two medium-access control schemes: synchronous array method (SAM and slotted ALOHA. The regular topologies considered are square, hexagon, and triangle. Both nonfading channels and Rayleigh fading channels are examined. Furthermore, both omnidirectional antennas and directional antennas are considered. Our analysis shows that the SAM leads to a much higher network throughput than the slotted ALOHA. The network throughput in this paper is measured in either bits-hops per second per Hertz per node or bits-meters per second per Hertz per node. The exact connection between the two measures is shown for each topology. With these two fundamental units, the network throughput shown in this paper can serve as a reliable benchmark for future works on network throughput of large networks.
Throughput Analysis of Large Wireless Networks with Regular Topologies
Directory of Open Access Journals (Sweden)
Kezhu Hong
2007-04-01
Full Text Available The throughput of large wireless networks with regular topologies is analyzed under two medium-access control schemes: synchronous array method (SAM and slotted ALOHA. The regular topologies considered are square, hexagon, and triangle. Both nonfading channels and Rayleigh fading channels are examined. Furthermore, both omnidirectional antennas and directional antennas are considered. Our analysis shows that the SAM leads to a much higher network throughput than the slotted ALOHA. The network throughput in this paper is measured in either bits-hops per second per Hertz per node or bits-meters per second per Hertz per node. The exact connection between the two measures is shown for each topology. With these two fundamental units, the network throughput shown in this paper can serve as a reliable benchmark for future works on network throughput of large networks.
Extracting labeled topological patterns from samples of networks.
Directory of Open Access Journals (Sweden)
Christoph Schmidt
Full Text Available An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.
Topology-function conservation in protein-protein interaction networks.
Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša
2015-05-15
Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.
Topology influences performance in the associative memory neural networks
International Nuclear Information System (INIS)
Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang
2006-01-01
To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures. In this Letter, we found that the memory performance of neural networks obtained through asynchronous updating from 'larger' nodes to 'smaller' nodes are better than asynchronous updating in random order, especially for the scale-free topology. The computational performance of associative memory neural networks linked by the above-mentioned network topologies with the same amounts of nodes (neurons) and edges (synapses) were studied respectively. Along with topologies becoming more random and less locally disordered, we will see that the performance of associative memory neural network is quite improved. By comparing, we show that the regular lattice and random network form two extremes in terms of patterns stability and retrievability. For a network, its patterns stability and retrievability can be largely enhanced by adding a random component or some shortcuts to its structured component. According to the conclusions of this Letter, we can design the associative memory neural networks with high performance and minimal interconnect requirements
Topologically determined optimal stochastic resonance responses of spatially embedded networks
International Nuclear Information System (INIS)
Gosak, Marko; Marhl, Marko; Korosak, Dean
2011-01-01
We have analyzed the stochastic resonance phenomenon on spatial networks of bistable and excitable oscillators, which are connected according to their location and the amplitude of external forcing. By smoothly altering the network topology from a scale-free (SF) network with dominating long-range connections to a network where principally only adjacent oscillators are connected, we reveal that besides an optimal noise intensity, there is also a most favorable interaction topology at which the best correlation between the response of the network and the imposed weak external forcing is achieved. For various distributions of the amplitudes of external forcing, the optimal topology is always found in the intermediate regime between the highly heterogeneous SF network and the strong geometric regime. Our findings thus indicate that a suitable number of hubs and with that an optimal ratio between short- and long-range connections is necessary in order to obtain the best global response of a spatial network. Furthermore, we link the existence of the optimal interaction topology to a critical point indicating the transition from a long-range interactions-dominated network to a more lattice-like network structure.
Evaluation of Topology-Aware Broadcast Algorithms for Dragonfly Networks
Energy Technology Data Exchange (ETDEWEB)
Dorier, Matthieu; Mubarak, Misbah; Ross, Rob; Li, Jianping Kelvin; Carothers, Christopher D.; Ma, Kwan-Liu
2016-09-12
Two-tiered direct network topologies such as Dragonflies have been proposed for future post-petascale and exascale machines, since they provide a high-radix, low-diameter, fast interconnection network. Such topologies call for redesigning MPI collective communication algorithms in order to attain the best performance. Yet as increasingly more applications share a machine, it is not clear how these topology-aware algorithms will react to interference with concurrent jobs accessing the same network. In this paper, we study three topology-aware broadcast algorithms, including one designed by ourselves. We evaluate their performance through event-driven simulation for small- and large-sized broadcasts (in terms of both data size and number of processes). We study the effect of different routing mechanisms on the topology-aware collective algorithms, as well as their sensitivity to network contention with other jobs. Our results show that while topology-aware algorithms dramatically reduce link utilization, their advantage in terms of latency is more limited.
Context-Based Topology Control for Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Pragasen Mudali
2016-01-01
Full Text Available Topology Control has been shown to provide several benefits to wireless ad hoc and mesh networks. However these benefits have largely been demonstrated using simulation-based evaluations. In this paper, we demonstrate the negative impact that the PlainTC Topology Control prototype has on topology stability. This instability is found to be caused by the large number of transceiver power adjustments undertaken by the prototype. A context-based solution is offered to reduce the number of transceiver power adjustments undertaken without sacrificing the cumulative transceiver power savings and spatial reuse advantages gained from employing Topology Control in an infrastructure wireless mesh network. We propose the context-based PlainTC+ prototype and show that incorporating context information in the transceiver power adjustment process significantly reduces topology instability. In addition, improvements to network performance arising from the improved topology stability are also observed. Future plans to add real-time context-awareness to PlainTC+ will have the scheme being prototyped in a software-defined wireless mesh network test-bed being planned.
Deciphering the imprint of topology on nonlinear dynamical network stability
International Nuclear Information System (INIS)
Nitzbon, J; Schultz, P; Heitzig, J; Kurths, J; Hellmann, F
2017-01-01
Coupled oscillator networks show complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrised to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields. (paper)
Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics.
Prescott, Aaron M; McCollough, Forest W; Eldreth, Bryan L; Binder, Brad M; Abel, Steven M
2016-01-01
Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. The dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i) whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii) what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB). In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB). Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations, and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms underlying ethylene
Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics
Directory of Open Access Journals (Sweden)
Aaron M. Prescott
2016-08-01
Full Text Available Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. However, the dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB. In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB. Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms
Stabilization Strategies of Supply Networks with Stochastic Switched Topology
Directory of Open Access Journals (Sweden)
Shukai Li
2013-01-01
Full Text Available In this paper, a dynamical supply networks model with stochastic switched topology is presented, in which the stochastic switched topology is dependent on a continuous time Markov process. The goal is to design the state-feedback control strategies to stabilize the dynamical supply networks. Based on Lyapunov stability theory, sufficient conditions for the existence of state feedback control strategies are given in terms of matrix inequalities, which ensure the robust stability of the supply networks at the stationary states and a prescribed H∞ disturbance attenuation level with respect to the uncertain demand. A numerical example is given to illustrate the effectiveness of the proposed method.
Dynamical and topological aspects of consensus formation in complex networks
Chacoma, A.; Mato, G.; Kuperman, M. N.
2018-04-01
The present work analyzes a particular scenario of consensus formation, where the individuals navigate across an underlying network defining the topology of the walks. The consensus, associated to a given opinion coded as a simple message, is generated by interactions during the agent's walk and manifest itself in the collapse of the various opinions into a single one. We analyze how the topology of the underlying networks and the rules of interaction between the agents promote or inhibit the emergence of this consensus. We find that non-linear interaction rules are required to form consensus and that consensus is more easily achieved in networks whose degree distribution is narrower.
Small Worlds in the Tree Topologies of Wireless Sensor Networks
DEFF Research Database (Denmark)
Qiao, Li; Lingguo, Cui; Baihai, Zhang
2010-01-01
In this study, the characteristics of small worlds are investigated in the context of the tree topologies of wireless sensor networks. Tree topologies, which construct spatial graphs with larger characteristic path lengths than random graphs and small clustering coefficients, are ubiquitous...... in wireless sensor networks. Suffering from the link rewiring or the link addition, the characteristic path length of the tree topology reduces rapidly and the clustering coefficient increases greatly. The variety of characteristic path length influences the time synchronization characteristics of wireless...... sensor networks greatly. With the increase of the link rewiring or the link addition probability, the time synchronization error decreases drastically. Two novel protocols named LEACH-SW and TREEPSI-SW are proposed to improve the performances of the sensor networks, in which the small world...
Connected Dominating Set Based Topology Control in Wireless Sensor Networks
He, Jing
2012-01-01
Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…
Complex brain networks: From topological communities to clustered ...
Indian Academy of Sciences (India)
Abstract. Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. A challenging task is to understand the implications of such network structures on the functional organisation of the brain activ- ities. We investigate synchronisation dynamics on the ...
Exploiting Network Topology Information to Mitigate Ambiguities in VMP Localization
DEFF Research Database (Denmark)
Pedersen, Claus; Pedersen, Troels; Fleury, Bernard Henri
2011-01-01
We investigate an extension to the probabilistic model of a wireless sensor network (WSN) in the variational message passing localization algorithm. This extension exploits network topology information to mitigate ambiguities in WSN localization schemes. In a simulation case study we show that th...
Design and implementation of a topology control scheme for wireless mesh networks
CSIR Research Space (South Africa)
Mudali, P
2009-09-01
Full Text Available The Wireless Mesh Network (WMN) backbone is usually comprised of stationary nodes but the transient nature of wireless links results in changing network topologies. Topology Control (TC) aims to preserve network connectivity in ad hoc and mesh...
Topological dynamics of vortex-line networks in hexagonal manganites
Xue, Fei; Wang, Nan; Wang, Xueyun; Ji, Yanzhou; Cheong, Sang-Wook; Chen, Long-Qing
2018-01-01
The two-dimensional X Y model is the first well-studied system with topological point defects. On the other hand, although topological line defects are common in three-dimensional systems, the evolution mechanism of line defects is not fully understood. The six domains in hexagonal manganites converge to vortex lines in three dimensions. Using phase-field simulations, we predicted that during the domain coarsening process, the vortex-line network undergoes three types of basic topological changes, i.e., vortex-line loop shrinking, coalescence, and splitting. It is shown that the vortex-antivortex annihilation controls the scaling dynamics.
Topology detection for adaptive protection of distribution networks
Energy Technology Data Exchange (ETDEWEB)
Sachdev, M.S.; Sidhu, T.S.; Talukdar, B.K. [Univ. of Saskatchewan, Saskatoon, Saskatchewan (Canada). Power System Research Group
1995-12-31
A general purpose network topology detection technique suitable for use in adaptive relaying applications is presented in this paper. Three test systems were used to check the performance of the proposed technique. Results obtained from the tests are included. The proposed technique was implemented in the laboratory as a part of the implementation of the adaptive protection scheme. The execution times of the topology detection software were monitored and were found to be acceptable.
A Review of the Topologies Used in Smart Water Meter Networks: A Wireless Sensor Network Application
Marais, Jaco; Malekian, Reza; Ye, Ning; Wang, Ruchuan
2016-01-01
This paper presents several proposed and existing smart utility meter systems as well as their communication networks to identify the challenges of creating scalable smart water meter networks. Network simulations are performed on 3 network topologies (star, tree, and mesh) to determine their suitability for smart water meter networks. The simulations found that once a number of nodes threshold is exceeded the network’s delay increases dramatically regardless of implemented topology. This thr...
Topology design and performance analysis of an integrated communication network
Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.
1985-01-01
A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.
A network perspective on the topological importance of enzymes and their phylogenetic conservation
Directory of Open Access Journals (Sweden)
Jordán Ferenc
2007-04-01
Full Text Available Abstract Background A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes. Results Enzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance. Conclusion Our analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon
On the topological structure of multinationals network
Joyez, Charlie
2017-05-01
This paper uses a weighted network analysis to examine the structure of multinationals' implantation countries network. Based on French firm-level dataset of multinational enterprises (MNEs) the network analysis provides information on each country position in the network and in internationalization strategies of French MNEs through connectivity preferences among the nodes. The paper also details network-wide features and their recent evolution toward a more decentralized structure. While much has been said on international trade network, this paper shows that multinational firms' studies would also benefit from network analysis, notably by investigating the sensitivity of the network construction to firm heterogeneity.
Robustness Analysis of Real Network Topologies Under Multiple Failure Scenarios
DEFF Research Database (Denmark)
Manzano, M.; Marzo, J. L.; Calle, E.
2012-01-01
on topological characteristics. Recently approaches also consider the services supported by such networks. In this paper we carry out a robustness analysis of five real backbone telecommunication networks under defined multiple failure scenarios, taking into account the consequences of the loss of established......Nowadays the ubiquity of telecommunication networks, which underpin and fulfill key aspects of modern day living, is taken for granted. Significant large-scale failures have occurred in the last years affecting telecommunication networks. Traditionally, network robustness analysis has been focused...... connections. Results show which networks are more robust in response to a specific type of failure....
Robust network topologies for generating switch-like cellular responses.
Directory of Open Access Journals (Sweden)
Najaf A Shah
2011-06-01
Full Text Available Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity and extent of memory (bistability. Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability. Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28% and bistability (up to 18%; strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3% or bistable (up to 1% responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.
Evolution of robust network topologies: emergence of central backbones.
Peixoto, Tiago P; Bornholdt, Stefan
2012-09-14
We model the robustness against random failure or an intentional attack of networks with an arbitrary large-scale structure. We construct a block-based model which incorporates--in a general fashion--both connectivity and interdependence links, as well as arbitrary degree distributions and block correlations. By optimizing the percolation properties of this general class of networks, we identify a simple core-periphery structure as the topology most robust against random failure. In such networks, a distinct and small "core" of nodes with higher degree is responsible for most of the connectivity, functioning as a central "backbone" of the system. This centralized topology remains the optimal structure when other constraints are imposed, such as a given fraction of interdependence links and fixed degree distributions. This distinguishes simple centralized topologies as the most likely to emerge, when robustness against failure is the dominant evolutionary force.
Topology for efficient information dissemination in ad-hoc networking
Jennings, E.; Okino, C. M.
2002-01-01
In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.
Topology-induced critical current enhancement in Josephson networks
International Nuclear Information System (INIS)
Silvestrini, P.; Russo, R.; Corato, V.; Ruggiero, B.; Granata, C.; Rombetto, S.; Russo, M.; Cirillo, M.; Trombettoni, A.; Sodano, P.
2007-01-01
We investigate the properties of Josephson junction networks with inhomogeneous architecture. The networks are shaped as 'square comb' planar lattices on which Josephson junctions link superconducting islands arranged in the plane to generate the pertinent topology. Compared to the behavior of reference linear arrays, the temperature dependencies of the Josephson currents of the branches of the network exhibit relevant differences. The observed phenomena evidence new and surprising behavior of superconducting Josephson arrays
Molecular network topology and reliability for multipurpose diagnosis
Directory of Open Access Journals (Sweden)
Jalil MA
2011-10-01
Full Text Available MA Jalil1, N Moongfangklang2,3, K Innate4, S Mitatha3, J Ali5, PP Yupapin41Ibnu Sina Institute of Fundamental Science Studies, Nanotechnology Research Alliance, University of Technology Malaysia, Johor Bahru, Malaysia; 2School of Information and Communication Technology, Phayao University, Phayao, Thailand; 3Hybrid Computing Research Laboratory, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; 4Nanoscale Science and Engineering Research Alliance, Advanced Research Center for Photonics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; 5Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia, Johor Bahru, MalaysiaAbstract: This investigation proposes the use of molecular network topology for drug delivery and diagnosis network design. Three modules of molecular network topologies, such as bus, star, and ring networks, are designed and manipulated based on a micro- and nanoring resonator system. The transportation of the trapping molecules by light in the network is described and the theoretical background is reviewed. The quality of the network is analyzed and calculated in terms of signal transmission (ie, signal to noise ratio and crosstalk effects. Results obtained show that a bus network has advantages over star and ring networks, where the use of mesh networks is possible. In application, a thin film network can be fabricated in the form of a waveguide and embedded in artificial bone, which can be connected to the required drug targets. The particular drug/nutrient can be transported to the required targets via the particular network used.Keywords: molecular network, network reliability, network topology, drug network, multi-access network
Data center networks topologies, architectures and fault-tolerance characteristics
Liu, Yang; Veeraraghavan, Malathi; Lin, Dong; Hamdi, Mounir
2013-01-01
This SpringerBrief presents a survey of data center network designs and topologies and compares several properties in order to highlight their advantages and disadvantages. The brief also explores several routing protocols designed for these topologies and compares the basic algorithms to establish connections, the techniques used to gain better performance, and the mechanisms for fault-tolerance. Readers will be equipped to understand how current research on data center networks enables the design of future architectures that can improve performance and dependability of data centers. This con
Enumeration of minimal stoichiometric precursor sets in metabolic networks
Andrade, R.; Wannagat, M.; Coimbra Klein, C.; Acuna, V.; Marchetti Spaccamela, A.; Vieira Milreu, P.; Stougie, L.; Sagot, M.-F.
2016-01-01
Background: What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network
Coordination on networks: does topology matter?
Directory of Open Access Journals (Sweden)
Alberto Antonioni
Full Text Available Effective coordination is key to many situations that affect the well-being of two or more humans. Social coordination can be studied in coordination games between individuals located on networks of contacts. We study the behavior of humans in the laboratory when they play the Stag Hunt game - a game that has a risky but socially efficient equilibrium and an inefficient but safe equilibrium. We contrast behavior on a cliquish network to behavior on a random network. The cliquish network is highly clustered and resembles more closely to actual social networks than the random network. In contrast to simulations, we find that human players dynamics do not converge to the efficient outcome more often in the cliquish network than in the random network. Subjects do not use pure myopic best-reply as an individual update rule. Numerical simulations agree with laboratory results once we implement the actual individual updating rule that human subjects use in our laboratory experiments.
Topological Embedding Feature Based Resource Allocation in Network Virtualization
Directory of Open Access Journals (Sweden)
Hongyan Cui
2014-01-01
Full Text Available Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.
Exploring complex networks via topological embedding on surfaces.
Aste, Tomaso; Gramatica, Ruggero; Di Matteo, T
2012-09-01
We demonstrate that graphs embedded on surfaces are a powerful and practical tool to generate, to characterize, and to simulate networks with a broad range of properties. Any network can be embedded on a surface with sufficiently high genus and therefore the study of topologically embedded graphs is non-restrictive. We show that the local properties of the network are affected by the surface genus which determines the average degree, which influences the degree distribution, and which controls the clustering coefficient. The global properties of the graph are also strongly affected by the surface genus which is constraining the degree of interwovenness, changing the scaling properties of the network from large-world kind (small genus) to small- and ultrasmall-world kind (large genus). Two elementary moves allow the exploration of all networks embeddable on a given surface and naturally introduce a tool to develop a statistical mechanics description for these networks. Within such a framework, we study the properties of topologically embedded graphs which dynamically tend to lower their energy towards a ground state with a given reference degree distribution. We show that the cooling dynamics between high and low "temperatures" is strongly affected by the surface genus with the manifestation of a glass-like transition occurring when the distance from the reference distribution is low. We prove, with examples, that topologically embedded graphs can be built in a way to contain arbitrary complex networks as subgraphs. This method opens a new avenue to build geometrically embedded networks on hyperbolic manifolds.
Topology of the Erasmus student mobility network
Derzsi, A.; Derzsy, N.; Káptalan, E.; Néda, Z.
2011-07-01
The collaboration network generated by the Erasmus student mobilities in the year 2003 is analyzed and modeled. Nodes of this bipartite network are European universities and links are the Erasmus mobilities between these universities. This network is a complex directed and weighted graph. The non-directed and non-weighted projection of this network does not exhibit a scale-free nature, but proves to be a small-word type random network with a giant component. The connectivity data indicates an exponential degree distribution, a relatively high clustering coefficient and a small radius. It can be easily modeled by using a simple configuration model and arguing the exponential degree distribution. The weighted and directed version of the network can also be described by means of simple random network models.
Controllability in cancer metabolic networks according to drug targets as driver nodes.
Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali
2013-01-01
Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.
Discriminative topological features reveal biological network mechanisms
Directory of Open Access Journals (Sweden)
Levovitz Chaya
2004-11-01
Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.
Topological Analysis of Wireless Networks (TAWN)
2016-05-31
Bonnet theorem holds in geometric realizations of triangulated surfaces, the question is exactly how well it works in our network models. Based on some...Dataset 20150728 consists of a family of circular networks with a fixed number of nodes placed along circles of various radii and with various levels of...but did not complete, a family of embedded networks with a variable negative curvature bound. 5. Important findings and conclusions This project was
Characterization of emergent synaptic topologies in noisy neural networks
Miller, Aaron James
Learned behaviors are one of the key contributors to an animal's ultimate survival. It is widely believed that the brain's microcircuitry undergoes structural changes when a new behavior is learned. In particular, motor learning, during which an animal learns a sequence of muscular movements, often requires precisely-timed coordination between muscles and becomes very natural once ingrained. Experiments show that neurons in the motor cortex exhibit precisely-timed spike activity when performing a learned motor behavior, and constituent stereotypical elements of the behavior can last several hundred milliseconds. The subject of this manuscript concerns how organized synaptic structures that produce stereotypical spike sequences emerge from random, dynamical networks. After a brief introduction in Chapter 1, we begin Chapter 2 by introducing a spike-timing-dependent plasticity (STDP) rule that defines how the activity of the network drives changes in network topology. The rule is then applied to idealized networks of leaky integrate-and-fire neurons (LIF). These neurons are not subjected to the variability that typically characterize neurons in vivo. In noiseless networks, synapses develop closed loops of strong connectivity that reproduce stereotypical, precisely-timed spike patterns from an initially random network. We demonstrate the characteristics of the asymptotic synaptic configuration are dependent on the statistics of the initial random network. The spike timings of the neurons simulated in Chapter 2 are generated exactly by a computationally economical, nonlinear mapping which is extended to LIF neurons injected with fluctuating current in Chapter 3. Development of an economical mapping that incorporates noise provides a practical solution to the long simulation times required to produce asymptotic synaptic topologies in networks with STDP in the presence of realistic neuronal variability. The mapping relies on generating numerical solutions to the dynamics
Impact of network topology on synchrony of oscillatory power grids.
Rohden, Martin; Sorge, Andreas; Witthaut, Dirk; Timme, Marc
2014-03-01
Replacing conventional power sources by renewable sources in current power grids drastically alters their structure and functionality. In particular, power generation in the resulting grid will be far more decentralized, with a distinctly different topology. Here, we analyze the impact of grid topologies on spontaneous synchronization, considering regular, random, and small-world topologies and focusing on the influence of decentralization. We model the consumers and sources of the power grid as second order oscillators. First, we analyze the global dynamics of the simplest non-trivial (two-node) network that exhibit a synchronous (normal operation) state, a limit cycle (power outage), and coexistence of both. Second, we estimate stability thresholds for the collective dynamics of small network motifs, in particular, star-like networks and regular grid motifs. For larger networks, we numerically investigate decentralization scenarios finding that decentralization itself may support power grids in exhibiting a stable state for lower transmission line capacities. Decentralization may thus be beneficial for power grids, regardless of the details of their resulting topology. Regular grids show a specific sharper transition not found for random or small-world grids.
Topological properties of random wireless networks
Indian Academy of Sciences (India)
http://www.ias.ac.in/article/fulltext/sadh/031/02/0117-0139 ... Abstract. Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). ... We then discuss some asymptotic results for networks in higher dimensions when the nodes are distributed in a ﬁnite volume. Finally ...
Topological properties of random wireless networks
Indian Academy of Sciences (India)
Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). In addition to their extensive application in the modelling of wireless networks, RGGs ﬁnd many new applications and are being studied in their own right. In this paper we ﬁrst provide a brief introduction to the ...
Network topology and interbank credit risk
International Nuclear Information System (INIS)
González-Avella, Juan Carlos; Hoffmann de Quadros, Vanessa; Iglesias, José Roberto
2016-01-01
Modern financial systems are greatly entangled. They exhibit a complex interdependence, including a network of bilateral exposures in the interbank market. The most frequent interaction consists in operations where institutions with surplus liquidity lend to those with a liquidity shortage. These loans may be interpreted as links between the banks and the links display features in some way representative of scale-free networks. While the interbank market is responsible for efficient liquidity allocation, it also introduces the possibility for systemic risk via financial contagion. Insolvency of one bank can propagate through links leading to insolvency of other banks. In this paper, we explore the characteristics of financial contagion in interbank networks whose distribution of links approaches a power law, as well as we improve previous models by introducing a simple mechanism to describe banks’ balance sheets, that are obtained from information on network connectivity. By varying the parameters for the creation of the network, several interbank networks are built, in which the concentration of debt and credit comes from the distribution of links. The results suggest that more connected networks that have a high concentration of credit are more resilient to contagion than other types of networks analyzed.
Statistical Inferences from the Topology of Complex Networks
2016-10-04
differences, and apply principal component analysis and support vector machines , or feed these results into a neural network. The secondary goal of the...support vector machines , or feed these results into a neural network. The secondary goal of the project was to help place Topological Data Analysis on a...included elsewhere such as: prepared in cooperation with; translation of; report supersedes; old edition number, etc. 14. ABSTRACT. A brief
LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.
Energy Technology Data Exchange (ETDEWEB)
MASLOV,S.SNEPPEN,K.
2003-11-17
Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.
Topological complexity of frictional interfaces: friction networks
Directory of Open Access Journals (Sweden)
H. O. Ghaffari
2012-03-01
Full Text Available Through research conducted in this study, a network approach to the correlation patterns of void spaces in rough fractures (crack type II was developed. We characterized friction networks with several networks characteristics. The correlation among network properties with the fracture permeability is the result of friction networks. The revealed hubs in the complex aperture networks confirmed the importance of highly correlated groups to conduct the highlighted features of the dynamical aperture field. We found that there is a universal power law between the nodes' degree and motifs frequency (for triangles it reads T(k ∝ kβ (β ≈ 2 ± 0.3. The investigation of localization effects on eigenvectors shows a remarkable difference in parallel and perpendicular aperture patches. Furthermore, we estimate the rate of stored energy in asperities so that we found that the rate of radiated energy is higher in parallel friction networks than it is in transverse directions. The final part of our research highlights 4 point sub-graph distribution and its correlation with fluid flow. For shear rupture, we observed a similar trend in sub-graph distribution, resulting from parallel and transversal aperture profiles (a superfamily phenomenon.
Connectivity, topology and dynamics in climate networks
Czech Academy of Sciences Publication Activity Database
Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin
2012-01-01
Roč. 14, - (2012), s. 8397 ISSN 1607-7962. [European Geosciences Union General Assembly 2012. 22.04.2012-27.04.2012, Vienna] R&D Projects: GA ČR GCP103/11/J068 Institutional support: RVO:67985807 Keywords : complex networks * climate network * connectivity * entropy rate * El Nino Southern Oscillation * North Atlantic Oscillation Subject RIV: BB - Applied Statistics, Operational Research
Analysis of Degree 5 Chordal Rings for Network Topologies
DEFF Research Database (Denmark)
Riaz, M. Tahir; Pedersen, Jens Myrup; Bujnowski, Sławomir
2011-01-01
This paper presents an analysis of degree 5 chordal rings, from a network topology point of view. The chordal rings are mainly evaluated with respect to average distance and diameter. We derive approximation expressions for the related ideal graphs, and show that these matches the real chordal......, we suggest that future research could deal with either finding degree 5 topologies with average distance and diameter closer to these of the reference graph, or to develop more realistic bounds than those presented by these reference graphs....
Custom Topology Generation for Network-on-Chip
DEFF Research Database (Denmark)
Stuart, Matthias Bo; Sparsø, Jens
2007-01-01
This paper compares simulated annealing and tabu search for generating custom topologies for applications with periodic behaviour executing on a network-on-chip. The approach differs from previous work by starting from a fixed mapping of IP-cores to routers and performing design space exploration...... around an initial topology. The tabu search has been modified from its normally encountered form to allow easier escaping from local minima. A number of synthetic benchmarks are used for tuning the parameters of both heuristics and for testing the quality of the solutions each heuristic produces...
Regime-dependent topological properties of biofuels networks
Czech Academy of Sciences Publication Activity Database
Krištoufek, Ladislav; Janda, K.; Zilberman, D.
2013-01-01
Roč. 86, č. 2 (2013), 40-1-40-12 ISSN 1434-6028 R&D Projects: GA ČR GA402/09/0965 Grant - others:GA UK(CZ) 118310; GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : topology * biofuels * correlations Subject RIV: AH - Economics Impact factor: 1.463, year: 2013 http://library.utia.cas.cz/separaty/2013/E/kristoufek-regime-dependent topological properties of biofuels networks.pdf
Topological Effects and Performance Optimization in Transportation Continuous Network Design
Directory of Open Access Journals (Sweden)
Jianjun Wu
2014-01-01
Full Text Available Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.
International Nuclear Information System (INIS)
Zhang Li-Sheng; Mi Yuan-Yuan; Gu Wei-Feng; Hu Gang
2014-01-01
All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend on network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks (ECNs) and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns (e.g., periodic, quasiperiodic, and chaotic). In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies (random or scale-free topologies) and different interaction structures (symmetric or asymmetric couplings). The mechanisms behind these differences are explained physically. (interdisciplinary physics and related areas of science and technology)
Effects of network resolution on topological properties of human neocortex
DEFF Research Database (Denmark)
Romero-Garcia, Rafael; Atienza, Mercedes; Clemmensen, Line Katrine Harder
2012-01-01
-world organization. The present study investigates how resolution of thickness-based cortical scales impacts on topological properties of human anatomical cortical networks. To this end, we designed a novel approach aimed at determining the best trade-off between small-world attributes of anatomical cortical...... regions). In summary, our study confirms that topological organization of anatomical cortical networks varies with both sparsity and resolution of cortical scale, and it further provides a novel methodological framework aimed at identifying cortical schemes that maximize small-worldness with the lowest......Graph theoretical analyses applied to neuroimaging datasets have provided valuable insights into the large-scale anatomical organization of the human neocortex. Most of these studies were performed with different cortical scales leading to cortical networks with different levels of small...
Statistical mechanics of polymer networks of any topology
International Nuclear Information System (INIS)
Duplantier, B.
1989-01-01
The statistical mechanics is considered of any polymer network with a prescribed topology, in dimension d, which was introduced previously. The basic direct renormalization theory of the associated continuum model is established. It has a very simple multiplicative structure in terms of the partition functions of the star polymers constituting the vertices of the network. A calculation is made to O(ε 2 ), where d = 4 -ε, of the basic critical dimensions σ L associated with any L=leg vertex (L ≥ 1). From this infinite series of critical exponents, any topology-dependent critical exponent can be derived. This is applied to the configuration exponent γ G of any network G to O(ε 2 ), including L-leg star polymers. The infinite sets of contact critical exponents θ between multiple points of polymers or between the cores of several star polymers are also deduced. As a particular case, the three exponents θ 0 , θ 1 , θ 2 calculated by des Cloizeaux by field-theoretic methods are recovered. The limiting exact logarithmic laws are derived at the upper critical dimension d = 4. The results are generalized to the series of topological exponents of polymer networks near a surface and of tricritical polymers at the Θ-point. Intersection properties of networks of random walks can be studied similarly. The above factorization theory of the partition function of any polymer network over its constituting L-vertices also applies to two dimensions, where it can be related to conformal invariance. The basic critical exponents σ L and thus any topological polymer exponents are then exactly known. Principal results published elsewhere are recalled
Radiation-Induced Topological Disorder in Irradiated Network Structures
Energy Technology Data Exchange (ETDEWEB)
Hobbs, Linn W.
2002-12-21
This report summarizes results of a research program investigating the fundamental principles underlying the phenomenon of topological disordering in a radiation environment. This phenomenon is known popularly as amorphization, but is more formally described as a process of radiation-induced structural arrangement that leads in crystals to loss of long-range translational and orientational correlations and in glasses to analogous alteration of connectivity topologies. The program focus has been on a set compound ceramic solids with directed bonding exhibiting structures that can be described as networks. Such solids include SiO2, Si3N4, SiC, which are of interest to applications in fusion energy production, nuclear waste storage, and device manufacture involving ion implantation or use in radiation fields. The principal investigative tools comprise a combination of experimental diffraction-based techniques, topological modeling, and molecular-dynamics simulations that have proven a rich source of information in the preceding support period. The results from the present support period fall into three task areas. The first comprises enumeration of the rigidity constraints applying to (1) more complex ceramic structures (such as rutile, corundum, spinel and olivine structures) that exhibit multiply polytopic coordination units or multiple modes of connecting such units, (2) elemental solids (such as graphite, silicon and diamond) for which a correct choice of polytope is necessary to achieve correct representation of the constraints, and (3) compounds (such as spinel and silicon carbide) that exhibit chemical disorder on one or several sublattices. With correct identification of the topological constraints, a unique correlation is shown to exist between constraint and amorphizability which demonstrates that amorphization occurs at a critical constraint loss. The second task involves the application of molecular dynamics (MD) methods to topologically-generated models
Radiation-Induced Topological Disorder in Irradiated Network Structures
International Nuclear Information System (INIS)
Hobbs, Linn W.
2002-12-01
This report summarizes results of a research program investigating the fundamental principles underlying the phenomenon of topological disordering in a radiation environment. This phenomenon is known popularly as amorphization, but is more formally described as a process of radiation-induced structural arrangement that leads in crystals to loss of long-range translational and orientational correlations and in glasses to analogous alteration of connectivity topologies. The program focus has been on a set compound ceramic solids with directed bonding exhibiting structures that can be described as networks. Such solids include SiO2, Si3N4, SiC, which are of interest to applications in fusion energy production, nuclear waste storage, and device manufacture involving ion implantation or use in radiation fields. The principal investigative tools comprise a combination of experimental diffraction-based techniques, topological modeling, and molecular-dynamics simulations that have proven a rich source of information in the preceding support period. The results from the present support period fall into three task areas. The first comprises enumeration of the rigidity constraints applying to (1) more complex ceramic structures (such as rutile, corundum, spinel and olivine structures) that exhibit multiply polytopic coordination units or multiple modes of connecting such units, (2) elemental solids (such as graphite, silicon and diamond) for which a correct choice of polytope is necessary to achieve correct representation of the constraints, and (3) compounds (such as spinel and silicon carbide) that exhibit chemical disorder on one or several sublattices. With correct identification of the topological constraints, a unique correlation is shown to exist between constraint and amorphizability which demonstrates that amorphization occurs at a critical constraint loss. The second task involves the application of molecular dynamics (MD) methods to topologically-generated models
The evolution of modularity in bacterial metabolic networks.
Kreimer, Anat; Borenstein, Elhanan; Gophna, Uri; Ruppin, Eytan
2008-05-13
Deciphering the modular organization of metabolic networks and understanding how modularity evolves have attracted tremendous interest in recent years. Here, we present a comprehensive large scale characterization of modularity across the bacterial tree of life, systematically quantifying the modularity of the metabolic networks of >300 bacterial species. Three main determinants of metabolic network modularity are identified. First, network size is an important topological determinant of network modularity. Second, several environmental factors influence network modularity, with endosymbionts and mammal-specific pathogens having lower modularity scores than bacterial species that occupy a wider range of niches. Moreover, even among the pathogens, those that alternate between two distinct niches, such as insect and mammal, tend to have relatively high metabolic network modularity. Third, horizontal gene transfer is an important force that contributes significantly to metabolic modularity. We additionally reconstruct the metabolic network of ancestral bacterial species and examine the evolution of modularity across the tree of life. This reveals a trend of modularity decrease from ancestors to descendants that is likely the outcome of niche specialization and the incorporation of peripheral metabolic reactions.
Topological properties of random wireless networks
Indian Academy of Sciences (India)
). Finally, in § 5 we provide an annotated .... using independent exponential spacings as follows. Let Yn,0,Yn,1,... ,Yn,n be i.i.d. ...... by the sensors or beacon nodes that help in the self organization of the network. The subgraphs specified by W ...
Topological Properties of Bank Networks:. the Case of Brazil
Tabak, Benjamin M.; Cajueiro, Daniel O.; Serra, Thiago R.
This paper investigates the topological properties of bank networks. We build the minimum spanning tree, which is based on the concept of ultrametricity, using the correlation matrix for a variety of banking variables. Empirical results suggest that the private and foreign banks tend to form clusters within the network. Furthermore, banks with different sizes are also strongly connected and tend to form clusters. These results are robust to the use of different variables to build the network, such as bank profitability, assets, equity, revenue and loans.
SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zhi Chen
2014-01-01
Full Text Available Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs. Self-organization feature map (SOFM neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the competitive learning among nodes, and takes the node residual energy and the distance to the neighbor nodes into account in the clustering process. In addition, the approach of dynamically adjusting the transmitting power of the cluster head nodes is adopted to optimize the network topology. Simulation results show that SOFMHTC may get a better energy-efficient performance and make more balanced energy consumption compared with some existing algorithms in WSNs.
Topology Counts: Force Distributions in Circular Spring Networks
Heidemann, Knut M.; Sageman-Furnas, Andrew O.; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F.; Wardetzky, Max
2018-02-01
Filamentous polymer networks govern the mechanical properties of many biological materials. Force distributions within these networks are typically highly inhomogeneous, and, although the importance of force distributions for structural properties is well recognized, they are far from being understood quantitatively. Using a combination of probabilistic and graph-theoretical techniques, we derive force distributions in a model system consisting of ensembles of random linear spring networks on a circle. We show that characteristic quantities, such as the mean and variance of the force supported by individual springs, can be derived explicitly in terms of only two parameters: (i) average connectivity and (ii) number of nodes. Our analysis shows that a classical mean-field approach fails to capture these characteristic quantities correctly. In contrast, we demonstrate that network topology is a crucial determinant of force distributions in an elastic spring network. Our results for 1D linear spring networks readily generalize to arbitrary dimensions.
Topology dependent epidemic spreading velocity in weighted networks
International Nuclear Information System (INIS)
Duan, Wei; Qiu, Xiaogang; Quax, Rick; Lees, Michael; Sloot, Peter M A
2014-01-01
Many diffusive processes occur on structured networks with weighted links, such as disease spread by airplane transport or information diffusion in social networks or blogs. Understanding the impact of weight-connectivity correlations on epidemic spreading in weighted networks is crucial to support decision-making on disease control and other diffusive processes. However, a real understanding of epidemic spreading velocity in weighted networks is still lacking. Here we conduct a numerical study of the velocity of a Reed–Frost epidemic spreading process in various weighted network topologies as a function of the correlations between edge weights and node degrees. We find that a positive weight-connectivity correlation leads to a faster epidemic spreading compared to an unweighted network. In contrast, we find that both uncorrelated and negatively correlated weight distributions lead to slower spreading processes. In the case of positive weight-connectivity correlations, the acceleration of spreading velocity is weak when the heterogeneity of weight distribution increases. (paper)
Developmental time windows for axon growth influence neuronal network topology.
Lim, Sol; Kaiser, Marcus
2015-04-01
Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.
Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao
2015-01-01
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method. PMID:26161960
Directory of Open Access Journals (Sweden)
Shengqiao Ni
Full Text Available This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao
2015-01-01
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Distributed Energy-Efficient Topology Control Algorithm in Home M2M Networks
Lee, Chao-Yang; Yang, Chu-Sing
2012-01-01
Because machine-to-machine (M2M) technology enables machines to communicate with each other without human intervention, it could play a big role in sensor network systems. Through wireless sensor network (WSN) gateways, various information can be collected by sensors for M2M systems. For home M2M networks, this study proposes a distributed energy-efficient topology control algorithm for both topology construction and topology maintenance. Topology control is an effective method of enhancing e...
Topological isomorphisms of human brain and financial market networks.
Vértes, Petra E; Nicol, Ruth M; Chapman, Sandra C; Watkins, Nicholas W; Robertson, Duncan A; Bullmore, Edward T
2011-01-01
Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets - the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular - more highly optimized for information processing - than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets.
Topological isomorphisms of human brain and financial market networks
Directory of Open Access Journals (Sweden)
Petra E Vértes
2011-09-01
Full Text Available Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets - the timeseries of 90 stocks from the New York Stock Exchange over a three-year period, and the fMRI-derived timeseries acquired from 90 brain regions over the course of a 10 min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular - more highly optimised for information processing - than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph theoretically-mediated interface between systems neuroscience and the statistical physics of financial markets.
Static analysis of topology-dependent broadcast networks
DEFF Research Database (Denmark)
Nanz, Sebastian; Nielson, Flemming; Nielson, Hanne Riis
2010-01-01
changing network topology is a crucial ingredient. In this paper, we develop a static analysis that automatically constructs an abstract transition system, labelled by actions and connectivity information, to yield a mobility-preserving finite abstraction of the behaviour of a network expressed......Broadcast semantics poses significant challenges over point-to-point communication when it comes to formal modelling and analysis. Current approaches to analysing broadcast networks have focused on fixed connectivities, but this is unsuitable in the case of wireless networks where the dynamically...... in a process calculus with asynchronous local broadcast. Furthermore, we use model checking based on a 3-valued temporal logic to distinguish network behaviour which differs under changing connectivity patterns. (C) 2009 Elsevier Inc. All rights reserved....
Network synchronization: optimal and pessimal scale-free topologies
International Nuclear Information System (INIS)
Donetti, Luca; Hurtado, Pablo I; Munoz, Miguel A
2008-01-01
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability
Network synchronization: optimal and pessimal scale-free topologies
Energy Technology Data Exchange (ETDEWEB)
Donetti, Luca [Departamento de Electronica y Tecnologia de Computadores and Instituto de Fisica Teorica y Computacional Carlos I, Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain); Hurtado, Pablo I; Munoz, Miguel A [Departamento de Electromagnetismo y Fisica de la Materia and Instituto Carlos I de Fisica Teorica y Computacional Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain)], E-mail: mamunoz@onsager.ugr.es
2008-06-06
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability.
Topological network theory and its application to LCA and related industrial ecology tools
Heijungs, R.
2015-01-01
One way to study complex systems is by methods from topological network theory, that concentrate on the topological structure of the system in terms of vertices (nodes) and edges that connect pairs of vertices. The last two decades, topological network theory has seen a wide range of applications.
Integration of metabolome data with metabolic networks reveals reporter reactions
DEFF Research Database (Denmark)
Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen
2006-01-01
network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from......Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...
Hocking, John G
1988-01-01
""As textbook and reference work, this is a valuable addition to the topological literature."" - Mathematical ReviewsDesigned as a text for a one-year first course in topology, this authoritative volume offers an excellent general treatment of the main ideas of topology. It includes a large number and variety of topics from classical topology as well as newer areas of research activity.There are four set-theoretic chapters, followed by four primarily algebraic chapters. Chapter I covers the fundamentals of topological and metrical spaces, mappings, compactness, product spaces, the Tychonoff t
A topological pattern of urban street networks: Universality and peculiarity
Jiang, Bin
2007-10-01
In this paper, we derive a topological pattern of urban street networks using a large sample (the largest so far to the best of our knowledge) of 40 US cities and a few more from elsewhere of different sizes. It is found that all the topologies of urban street networks based on street-street intersection demonstrate a small world structure, and a scale-free property for both street length and connectivity degree. More specifically, for any street network, about 80% of its streets have length or degrees less than its average value, while 20% of streets have length or degrees greater than the average. Out of the 20%, there are less than 1% of streets which can form a backbone of the street network. Based on the finding, we conjecture that the 20% streets account for 80% of traffic flow, and the 1% streets constitute a cognitive map of the urban street network. We illustrate further a peculiarity about the scale-free property.
Learning Wireless Networks' Topologies Using Asymmetric Granger Causality
Laghate, Mihir; Cabric, Danijela
2018-02-01
Sharing spectrum with a communicating incumbent user (IU) network requires avoiding interference to IU receivers. But since receivers are passive when in the receive mode and cannot be detected, the network topology can be used to predict the potential receivers of a currently active transmitter. For this purpose, this paper proposes a method to detect the directed links between IUs of time multiplexing communication networks from their transmission start and end times. It models the response mechanism of commonly used communication protocols using Granger causality: the probability of an IU starting a transmission after another IU's transmission ends increases if the former is a receiver of the latter. This paper proposes a non-parametric test statistic for detecting such behavior. To help differentiate between a response and the opportunistic access of available spectrum, the same test statistic is used to estimate the response time of each link. The causal structure of the response is studied through a discrete time Markov chain that abstracts the IUs' medium access protocol and focuses on the response time and response probability of 2 IUs. Through NS-3 simulations, it is shown that the proposed algorithm outperforms existing methods in accurately learning the topologies of infrastructure-based networks and that it can infer the directed data flow in ad hoc networks with finer time resolution than an existing method.
Impact of network topology on self-organized criticality
Hoffmann, Heiko
2018-02-01
The general mechanisms behind self-organized criticality (SOC) are still unknown. Several microscopic and mean-field theory approaches have been suggested, but they do not explain the dependence of the exponents on the underlying network topology of the SOC system. Here, we first report the phenomena that in the Bak-Tang-Wiesenfeld (BTW) model, sites inside an avalanche area largely return to their original state after the passing of an avalanche, forming, effectively, critically arranged clusters of sites. Then, we hypothesize that SOC relies on the formation process of these clusters, and present a model of such formation. For low-dimensional networks, we show theoretically and in simulation that the exponent of the cluster-size distribution is proportional to the ratio of the fractal dimension of the cluster boundary and the dimensionality of the network. For the BTW model, in our simulations, the exponent of the avalanche-area distribution matched approximately our prediction based on this ratio for two-dimensional networks, but deviated for higher dimensions. We hypothesize a transition from cluster formation to the mean-field theory process with increasing dimensionality. This work sheds light onto the mechanisms behind SOC, particularly, the impact of the network topology.
Distributed topology control algorithm to conserve energy in heterogeneous wireless mesh networks
CSIR Research Space (South Africa)
Aron, FO
2008-07-01
Full Text Available to increasing the network capacity via better spatial bandwidth reuse. In this work, the problem of topology control in a hybrid WMN of heterogeneous wireless devices with varying maximum transmission ranges is considered. A localized distributed topology...
Sampling Algorithms of Pure Network Topologies: Stability and Separability of Metric Embeddings
National Research Council Canada - National Science Library
Airoldi, Edoardo M
2005-01-01
... has become a central theme for KDD. The intuition behind the plethora of approaches relies upon a few basic types of networks, which are identified by specific local and global topological properties, and which the authors term "pure" topology types...
Directory of Open Access Journals (Sweden)
Yinyin Yuan
Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.
Classification of data patterns using an autoassociative neural network topology
Dietz, W. E.; Kiech, E. L.; Ali, M.
1989-01-01
A diagnostic expert system based on neural networks is developed and applied to the real-time diagnosis of jet and rocket engines. The expert system methodologies are based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. The approach addresses deficiencies inherent in many feedforward neural network models and greatly reduces the number of networks necessary to identify the existence of a fault condition and estimate the duration and severity of the identified fault. The network topology used in the present implementation of the diagnostic system is described, as well as the training regimen used and the response of the system to inputs representing both previously observed and unknown fault scenarios. Noise effects on the integrity of the diagnosis are also evaluated.
Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher
2005-01-01
This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.
Virtual network embedding in cross-domain network based on topology and resource attributes
Zhu, Lei; Zhang, Zhizhong; Feng, Linlin; Liu, Lilan
2018-03-01
Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.
Self-organized topology of recurrence-based complex networks
International Nuclear Information System (INIS)
Yang, Hui; Liu, Gang
2013-01-01
With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks
Self-organized topology of recurrence-based complex networks.
Yang, Hui; Liu, Gang
2013-12-01
With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.
Directory of Open Access Journals (Sweden)
Marcio Luis Acencio
Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved
Toju, Hirokazu; Guimarães, Paulo R.; Olesen, Jens M.; Thompson, John N.
2015-01-01
In nature, plants and their pollinating and/or seed-dispersing animals form complex interaction networks. The commonly observed pattern of links between specialists and generalists in these networks has been predicted to promote species coexistence. Plants also build highly species-rich mutualistic networks below ground with root-associated fungi, and the structure of these plant–fungus networks may also affect terrestrial community processes. By compiling high-throughput DNA sequencing data sets of the symbiosis of plants and their root-associated fungi from three localities along a latitudinal gradient, we uncovered the entire network architecture of these interactions under contrasting environmental conditions. Each network included more than 30 plant species and hundreds of mycorrhizal and endophytic fungi belonging to diverse phylogenetic groups. The results were consistent with the notion that processes shaping host-plant specialization of fungal species generate a unique linkage pattern that strongly contrasts with the pattern of above-ground plant–partner networks. Specifically, plant–fungus networks lacked a “nested” architecture, which has been considered to promote species coexistence in plant–partner networks. Rather, the below-ground networks had a conspicuous “antinested” topology. Our findings lead to the working hypothesis that terrestrial plant community dynamics are likely determined by the balance between above-ground and below-ground webs of interspecific interactions. PMID:26601279
Frequency-specific network topologies in the resting human brain
Directory of Open Access Journals (Sweden)
Shuntaro eSasai
2014-12-01
Full Text Available A community is a set of nodes with dense inter-connections, while there are sparse connections between different communities. A hub is a highly connected node with high centrality. It has been shown that both communities and hubs exist simultaneously in the brain’s functional connectivity network, as estimated by correlations among low-frequency spontaneous fluctuations in functional magnetic resonance imaging (fMRI signal changes (0.01–0.10 Hz. This indicates that the brain has a spatial organization that promotes both segregation and integration of information. Here, we demonstrate that frequency-specific network topologies that characterize segregation and integration also exist within this frequency range. In investigating the coherence spectrum among 87 brain regions, we found that two frequency bands, 0.01–0.03 Hz (very low frequency [VLF] band and 0.07–0.09 Hz (low frequency [LF] band, mainly contributed to functional connectivity. Comparing graph theoretical indices for the VLF and LF bands revealed that the network in the former had a higher capacity for information segregation between identified communities than the latter. Hubs in the VLF band were mainly located within the anterior cingulate cortices, whereas those in the LF band were located in the posterior cingulate cortices and thalamus. Thus, depending on the timescale of brain activity, at least two distinct network topologies contributed to information segregation and integration. This suggests that the brain intrinsically has timescale-dependent functional organizations.
Foreign currency exchange network topology across the 2008 credit crisis
Sharif, Shamshuritawati; Ap, Nuraisah Che; Ruslan, Nuraimi
2017-05-01
A stable world currency exchange rate is a very important aspect to be considered for a developed country, i.e Malaysia. A better understanding about the currencies itself is needed nowadays. This project is about to understanding the fluctuation and to identify the most influential world currencies in the three different cases; before credit crisis, during credit crisis and after credit crisis. A network topology approach is use to examine the interrelationship between currencies based on correlation analysis. With this point of view, those relationships can be measured by a correlation structure among the currencies. The network can be analyse by filtering the important information using minimum spanning tree (MST) and interpret it using degree centrality as the centrality measure. This topology will give a useful guide to understand the behaviour and determine the most influential currency in the network as a part of a complex system. All currencies are compared among the three different cases; before credit crisis, during credit crisis and after credit crisis period. The result of this project shows that Unites State Dollar (USD), Brazilian Real (BRL), United Kingdom Pound (EUR) and Danish Krone (DKK) are the most influential currencies before the credit crisis period. With respect to during the credit crisis, New Zealand Dollar (NZD) dominates the network and it is followed by Singapore Dollar (SGD) for after the credit crisis period.
Topology reduction in deep convolutional feature extraction networks
Wiatowski, Thomas; Grohs, Philipp; Bölcskei, Helmut
2017-08-01
Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and 10,000s of nodes. Such network sizes entail significant computational complexity due to the large number of convolutions that need to be carried out; in addition, a large number of parameters needs to be learned and stored. Very deep and wide CNNs may therefore not be well suited to applications operating under severe resource constraints as is the case, e.g., in low-power embedded and mobile platforms. This paper aims at understanding the impact of CNN topology, specifically depth and width, on the network's feature extraction capabilities. We address this question for the class of scattering networks that employ either Weyl-Heisenberg filters or wavelets, the modulus non-linearity, and no pooling. The exponential feature map energy decay results in Wiatowski et al., 2017, are generalized to O(a-N), where an arbitrary decay factor a > 1 can be realized through suitable choice of the Weyl-Heisenberg prototype function or the mother wavelet. We then show how networks of fixed (possibly small) depth N can be designed to guarantee that ((1 - ɛ) · 100)% of the input signal's energy are contained in the feature vector. Based on the notion of operationally significant nodes, we characterize, partly rigorously and partly heuristically, the topology-reducing effects of (effectively) band-limited input signals, band-limited filters, and feature map symmetries. Finally, for networks based on Weyl-Heisenberg filters, we determine the prototype function bandwidth that minimizes - for fixed network depth N - the average number of operationally significant nodes per layer.
International Nuclear Information System (INIS)
Wei Duqu; Luo Xiaoshu
2007-01-01
In this paper, we investigate coherence resonance (CR) and noise-induced synchronization in Hindmarsh-Rose (HR) neural network with three different types of topologies: regular, random, and small-world. It is found that the additive noise can induce CR in HR neural network with different topologies and its coherence is optimized by a proper noise level. It is also found that as coupling strength increases the plateau in the measure of coherence curve becomes broadened and the effects of network topology is more pronounced simultaneously. Moreover, we find that increasing the probability p of the network topology leads to an enhancement of noise-induced synchronization in HR neurons network.
Enumeration of minimal stoichiometric precursor sets in metabolic networks.
Andrade, Ricardo; Wannagat, Martin; Klein, Cecilia C; Acuña, Vicente; Marchetti-Spaccamela, Alberto; Milreu, Paulo V; Stougie, Leen; Sagot, Marie-France
2016-01-01
What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network (topological precursor sets). Due to cycles and the stoichiometric values of the reactions, it is often not possible to produce the target(s) from a topological precursor set in the sense that there is no feasible flux. Although considering the stoichiometry makes the problem harder, it enables to obtain biologically reasonable precursor sets that we call stoichiometric. Recently a method to enumerate all minimal stoichiometric precursor sets was proposed in the literature. The relationship between topological and stoichiometric precursor sets had however not yet been studied. Such relationship between topological and stoichiometric precursor sets is highlighted. We also present two algorithms that enumerate all minimal stoichiometric precursor sets. The first one is of theoretical interest only and is based on the above mentioned relationship. The second approach solves a series of mixed integer linear programming problems. We compared the computed minimal precursor sets to experimentally obtained growth media of several Escherichia coli strains using genome-scale metabolic networks. The results show that the second approach efficiently enumerates minimal precursor sets taking stoichiometry into account, and allows for broad in silico studies of strains or species interactions that may help to understand e.g. pathotype and niche-specific metabolic capabilities. sasita is written in Java, uses cplex as LP solver and can be downloaded together with all networks and input files used in this paper at http://www.sasita.gforge.inria.fr.
Kuratowski, Kazimierz
1968-01-01
Topology, Volume II deals with topology and covers topics ranging from compact spaces and connected spaces to locally connected spaces, retracts, and neighborhood retracts. Group theory and some cutting problems are also discussed, along with the topology of the plane. Comprised of seven chapters, this volume begins with a discussion on the compactness of a topological space, paying particular attention to Borel, Lebesgue, Riesz, Cantor, and Bolzano-Weierstrass conditions. Semi-continuity and topics in dimension theory are also considered. The reader is then introduced to the connecte
A compendium of inborn errors of metabolism mapped onto the human metabolic network.
Sahoo, Swagatika; Franzson, Leifur; Jonsson, Jon J; Thiele, Ines
2012-10-01
Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.
Topology and computational performance of attractor neural networks
International Nuclear Information System (INIS)
McGraw, Patrick N.; Menzinger, Michael
2003-01-01
To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies. The random configuration is the most efficient for storage and retrieval of patterns by the network as a whole. However, in the scale-free case retrieval errors are not distributed uniformly among the nodes. The portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. The implications of these findings for brain function and social dynamics are suggestive
Topological properties of complex networks in protein structures
Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik
2014-03-01
We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).
Employing Deceptive Dynamic Network Topology Through Software-Defined Networking
2014-03-01
Request For Comments RIP Routing Information Protocol RIPE NCC Reseaux IP Europeans Network Coordination Center RIR Regional Internet Registries RTT...Another large research project in building Internet measurement infrastructure is from the Reseaux IP Europeans Network Coordination Center (RIPE NCC) [24
Topology identification of the complex networks with non-delayed and delayed coupling
International Nuclear Information System (INIS)
Guo Wanli; Chen Shihua; Sun Wen
2009-01-01
In practical situation, there exists many uncertain information in complex networks, such as the topological structures. So the topology identification is an important issue in the research of the complex networks. Based on LaSalle's invariance principle, in this Letter, an adaptive controlling method is proposed to identify the topology of a weighted general complex network model with non-delayed and delayed coupling. Finally, simulation results show that the method is effective.
Adaptive approach to global synchronization of directed networks with fast switching topologies
International Nuclear Information System (INIS)
Qin Buzhi; Lu Xinbiao
2010-01-01
Global synchronization of directed networks with switching topologies is investigated. It is found that if there exists at least one directed spanning tree in the network with the fixed time-average topology and the time-average topology is achieved sufficiently fast, the network will reach global synchronization for appreciate coupling strength. Furthermore, this appreciate coupling strength may be obtained by local adaptive approach. A sufficient condition about the global synchronization is given. Numerical simulations verify the effectiveness of the adaptive strategy.
A method of network topology optimization design considering application process characteristic
Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo
2018-03-01
Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.
Brain Structural Covariance Network Topology in Remitted Posttraumatic Stress Disorder
Directory of Open Access Journals (Sweden)
Delin Sun
2018-03-01
Full Text Available Posttraumatic stress disorder (PTSD is a prevalent, chronic disorder with high psychiatric morbidity; however, a substantial portion of affected individuals experience remission after onset. Alterations in brain network topology derived from cortical thickness correlations are associated with PTSD, but the effects of remitted symptoms on network topology remain essentially unexplored. In this cross-sectional study, US military veterans (N = 317 were partitioned into three diagnostic groups, current PTSD (CURR-PTSD, N = 101, remitted PTSD with lifetime but no current PTSD (REMIT-PTSD, N = 35, and trauma-exposed controls (CONTROL, n = 181. Cortical thickness was assessed for 148 cortical regions (nodes and suprathreshold interregional partial correlations across subjects constituted connections (edges in each group. Four centrality measures were compared with characterize between-group differences. The REMIT-PTSD and CONTROL groups showed greater centrality in left frontal pole than the CURR-PTSD group. The REMIT-PTSD group showed greater centrality in right subcallosal gyrus than the other two groups. Both REMIT-PTSD and CURR-PTSD groups showed greater centrality in right superior frontal sulcus than CONTROL group. The centrality in right subcallosal gyrus, left frontal pole, and right superior frontal sulcus may play a role in remission, current symptoms, and PTSD history, respectively. The network centrality changes in critical brain regions and structural networks are associated with remitted PTSD, which typically coincides with enhanced functional behaviors, better emotion regulation, and improved cognitive processing. These brain regions and associated networks may be candidates for developing novel therapies for PTSD. Longitudinal work is needed to characterize vulnerability to chronic PTSD, and resilience to unremitting PTSD.
Observations and analysis of self-similar branching topology in glacier networks
Bahr, D.B.; Peckham, S.D.
1996-01-01
Glaciers, like rivers, have a branching structure which can be characterized by topological trees or networks. Probability distributions of various topological quantities in the networks are shown to satisfy the criterion for self-similarity, a symmetry structure which might be used to simplify future models of glacier dynamics. Two analytical methods of describing river networks, Shreve's random topology model and deterministic self-similar trees, are applied to the six glaciers of south central Alaska studied in this analysis. Self-similar trees capture the topological behavior observed for all of the glaciers, and most of the networks are also reasonably approximated by Shreve's theory. Copyright 1996 by the American Geophysical Union.
Local, distributed topology control for large-scale wireless ad-hoc networks
Nieberg, T.; Hurink, Johann L.
In this document, topology control of a large-scale, wireless network by a distributed algorithm that uses only locally available information is presented. Topology control algorithms adjust the transmission power of wireless nodes to create a desired topology. The algorithm, named local power
Classification of complex networks based on similarity of topological network features
Attar, Niousha; Aliakbary, Sadegh
2017-09-01
Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.
Topological resilience in non-normal networked systems
Asllani, Malbor; Carletti, Timoteo
2018-04-01
The network of interactions in complex systems strongly influences their resilience and the system capability to resist external perturbations or structural damages and to promptly recover thereafter. The phenomenon manifests itself in different domains, e.g., parasitic species invasion in ecosystems or cascade failures in human-made networks. Understanding the topological features of the networks that affect the resilience phenomenon remains a challenging goal for the design of robust complex systems. We hereby introduce the concept of non-normal networks, namely networks whose adjacency matrices are non-normal, propose a generating model, and show that such a feature can drastically change the global dynamics through an amplification of the system response to exogenous disturbances and eventually impact the system resilience. This early stage transient period can induce the formation of inhomogeneous patterns, even in systems involving a single diffusing agent, providing thus a new kind of dynamical instability complementary to the Turing one. We provide, first, an illustrative application of this result to ecology by proposing a mechanism to mute the Allee effect and, second, we propose a model of virus spreading in a population of commuters moving using a non-normal transport network, the London Tube.
A Review of the Topologies Used in Smart Water Meter Networks: A Wireless Sensor Network Application
Directory of Open Access Journals (Sweden)
Jaco Marais
2016-01-01
Full Text Available This paper presents several proposed and existing smart utility meter systems as well as their communication networks to identify the challenges of creating scalable smart water meter networks. Network simulations are performed on 3 network topologies (star, tree, and mesh to determine their suitability for smart water meter networks. The simulations found that once a number of nodes threshold is exceeded the network’s delay increases dramatically regardless of implemented topology. This threshold is at a relatively low number of nodes (50 and the use of network topologies such as tree or mesh helps alleviate this problem and results in lower network delays. Further simulations found that the successful transmission of application layer packets in a 70-end node tree network can be improved by 212% when end nodes only transmit data to their nearest router node. The relationship between packet success rate and different packet sizes was also investigated and reducing the packet size with a factor of 16 resulted in either 156% or 300% increases in the amount of successfully received packets depending on the network setup.
Topological dimension tunes activity patterns in hierarchical modular networks
Safari, Ali; Moretti, Paolo; Muñoz, Miguel A.
2017-11-01
Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both segregation and integration of neural information. Here, we propose an extensive analysis of the critical spreading rate (or ‘epidemic’ threshold)—separating a phase with endemic persistent activity from one in which activity ceases—on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs and, most importantly, we elucidate the role played by the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.
Development of Active External Network Topology Module for Floodlight SDN Controller
Directory of Open Access Journals (Sweden)
A. A. Noskov
2015-01-01
Full Text Available Traditional network architecture is inflexible and complicated. This observation has led to a paradigm shift towards software-defined networking (SDN, where network management level is separated from data forwarding level. This change was made possible by control plane transfer from the switching equipment to software modules that run on a dedicated server, called the controller (or network operating system, or network applications, that work with this controller. Methods of representation, storage and communication interfaces with network topology elements are the most important aspects of network operating systems available to SDN user because performance of some key controller modules is heavily dependent on internal representation of the network topology. Notably, firewall and routing modules are examples of such modules. This article describes the methods used for presentation and storage of network topologies, as well as interface to the corresponding Floodlight modules. An alternative algorithm has been suggested and developed for message exchange conveying network topology alterations between the controller and network applications. Proposed algorithm makes implementation of module alerting based on subscription to the relevant events. API for interaction between controller and network applications has been developed. This algorithm and API formed the base for Topology Tracker module capable to inform network applications about the changes that had occurred in the network topology and also stores compact representation of the network to speed up the interaction process.
Yuan, Binke; Fang, Yuxing; Han, Zaizhu; Song, Luping; He, Yong; Bi, Yanchao
2017-12-20
Various important topological properties of healthy brain connectome have recently been identified. However, the manner in which brain lesion changes the functional network topology is unknown. We examined how critical specific brain areas are in the maintenance of network topology using multivariate support vector regression analysis on brain structural and resting-state functional imaging data in 96 patients with brain damages. Patients' cortical lesion distribution patterns could significantly predict the functional network topology and a set of regions with significant weights in the prediction models were identified as "lesion hubs". Intriguingly, we found two different types of lesion hubs, whose lesions associated with changes of network topology towards relatively different directions, being either more integrated (global) or more segregated (local), and correspond to hubs identified in healthy functional network in complex manners. Our results pose further important questions about the potential dynamics of the functional brain network after brain damage.
Interdependent networks - Topological percolation research and application in finance
Zhou, Di
This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks and applying simulation and numerical methods to study the robustness of the network system, and ii) applying statistical physics concepts and methods to quantitatively analyze complex systems and applying the theoretical framework to study real-world systems. In part I, we focus on developing theories of interdependent networks as well as building computer simulation models, which includes three parts: 1) We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the networks significantly changes the critical density of failures, which can trigger the total disruption of the two-network system. Specifically, we find that the assortativity within a single network decreases the robustness of the entire system. 2) We study the percolation behavior of two interdependent scale-free (SF) networks under random failure of 1-p fraction of nodes. We find that as the coupling strength q between the two networks reduces from 1 (fully coupled) to 0 (no coupling), there exist two critical coupling strengths q1 and q2 , which separate the behaviors of the giant component as a function of p into three different regions, and for q2 stock market indices and foreign exchange daily returns for 60 countries over the period of 1999-2012. We build a multi-layer network model based on different correlation measures, and introduce a dynamic network model to simulate and analyze the initializing and spreading of financial crisis. Using different computational approaches and econometric tests, we find atypical behavior of the cross correlations and community formations in the financial networks that we study during the financial crisis of 2008. For example, the overall correlation of stock market increases during crisis while the correlation between
Increasing the cost-constrained availability of WDM Networks with Degree-3 Structured Topologies
DEFF Research Database (Denmark)
Gutierrez Lopez, Jose Manuel; Georgakilas, Kostas; Katrinis, Kostas
2010-01-01
This paper studies the impact of node degree distribution to availability and capital and deployment expenditure of optical WDM transport networks. Three structured degree-3 graphs with fair node degree distribution are proposed in this context. Using a realistic network optimization framework...... based on genetic algorithms, we evaluate the performance of structured topologies and compare it against a practical topology (NSFNET). The results manifest that nodal degree fairness leads to increased availability compared to conventional topologies, while not incurring higher capital and deployment...
WenJun Zhang; QuHuan Li
2014-01-01
In present study we used self-organizing map (SOM) neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of ...
Comparing the Reliability of Regular Topologies on a Backbone Network. A Case Study
DEFF Research Database (Denmark)
Cecilio, Sergio Labeage; Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir
2009-01-01
The aim of this paper is to compare the reliability of regular topologies on a backbone network. The study is focused on a large-scale fiberoptic network. Different regular topological solutions as single ring, double ring or 4-Regular grid are applied to the case study, and compared in terms...
Profiling metabolic networks to study cancer metabolism.
Hiller, Karsten; Metallo, Christian M
2013-02-01
Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention. Copyright © 2012 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Yu, Hao; Yan, Ying; Berger, Michael Stubert
2009-01-01
of Service (QoS) provisioning abilities, which guarantee end-to-end performances of voice, video and data traffic delivered over networks. This paper introduces a topology-based hierarchical scheduler scheme, which controls the incoming traffic at the edge of the network based on the network topology....... This work has been carried out as a part of the research project HIPT (High quality IP network for IPTV and VoIP) founded by Danish Advanced Technology Foundation....
Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks
Giovanni Francesco Santonastaso; Armando Di Nardo; Michele Di Natale; Carlo Giudicianni; Roberto Greco
2018-01-01
Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, ma...
Directory of Open Access Journals (Sweden)
Zixuan Cang
2017-07-01
Full Text Available Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH method. ESPH represents 3D complex geometry by one-dimensional (1D topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN. We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes.weilab.math.msu.edu/TDL/.
The effects of deep network topology on mortality prediction.
Hao Du; Ghassemi, Mohammad M; Mengling Feng
2016-08-01
Deep learning has achieved remarkable results in the areas of computer vision, speech recognition, natural language processing and most recently, even playing Go. The application of deep-learning to problems in healthcare, however, has gained attention only in recent years, and it's ultimate place at the bedside remains a topic of skeptical discussion. While there is a growing academic interest in the application of Machine Learning (ML) techniques to clinical problems, many in the clinical community see little incentive to upgrade from simpler methods, such as logistic regression, to deep learning. Logistic regression, after all, provides odds ratios, p-values and confidence intervals that allow for ease of interpretation, while deep nets are often seen as `black-boxes' which are difficult to understand and, as of yet, have not demonstrated performance levels far exceeding their simpler counterparts. If deep learning is to ever take a place at the bedside, it will require studies which (1) showcase the performance of deep-learning methods relative to other approaches and (2) interpret the relationships between network structure, model performance, features and outcomes. We have chosen these two requirements as the goal of this study. In our investigation, we utilized a publicly available EMR dataset of over 32,000 intensive care unit patients and trained a Deep Belief Network (DBN) to predict patient mortality at discharge. Utilizing an evolutionary algorithm, we demonstrate automated topology selection for DBNs. We demonstrate that with the correct topology selection, DBNs can achieve better prediction performance compared to several bench-marking methods.
Exploration of the topology of chemical spaces with network measures.
Krein, Michael P; Sukumar, N
2011-11-17
Discontinuous changes in molecular structure (resulting from continuous transformations of molecular coordinates) lead to changes in chemical properties and biological activities that chemists attempt to describe through structure-activity or structure-property relationships (QSAR/QSPR). Such relationships are commonly envisioned in a continuous high-dimensional space of numerical descriptors, referred to as chemistry space. The choice of descriptors defining coordinates within chemistry space and the choice of similarity metrics thus influence the partitioning of this space into regions corresponding to local structural similarity. These are the regions (known as domains of applicability) most likely to be successfully modeled by a structure-activity relationship. In this work the network topology and scaling relationships of chemistry spaces are first investigated independent of a specific biological activity. Chemistry spaces studied include the ZINC data set, a qHTS PubChem bioassay, as well as the space of protein binding sites from the PDB. The characteristics of these networks are compared and contrasted with those of the bioassay SALI subnetwork, which maps discontinuities or cliffs in the structure-activity landscape. Mapping the locations of activity cliffs and comparing the global characteristics of SALI subnetworks with those of the underlying chemistry space networks generated using different representations, can guide the choice of a better representation. A higher local density of SALI edges with a particular representation indicates a more challenging structure-activity relationship using that fingerprint in that region of chemistry space.
Accepting Hybrid Networks of Evolutionary Processors with Special Topologies and Small Communication
Jürgen Dassow; Florin Manea
2010-01-01
Starting from the fact that complete Accepting Hybrid Networks of Evolutionary Processors allow much communication between the nodes and are far from network structures used in practice, we propose in this paper three network topologies that restrict the communication: star networks, ring networks, and grid networks. We show that ring-AHNEPs can simulate 2-tag systems, thus we deduce the existence of a universal ring-AHNEP. For star networks or grid networks, we show a more general result; th...
Topology of Wireless Sensor Network Based on the Perception Needs of the Internet of Things
Directory of Open Access Journals (Sweden)
Wu Yuanjun
2018-02-01
Full Text Available In order to adapt to the characteristics of multi network convergence of IOT, this paper sorts out and summarizes the development status and existing problems of Internet of things (IOT and wireless sensor network (WSN as well as the key points for future research. Based on the existing research, the wireless sensor topology SORCA protocol is improved, and an energy saving intrusion tolerant network topology SORCA-W generation method is proposed, which realizes the interconnection of multiple sensor networks. Firstly, according to the location of nodes, the network is divided into topology network composed of hexagonal units. Through adding different network nodes collection summary, the neighbor table is judged and modified, and the name of the network is added. As a result, it is concluded that a plurality of wireless sensor networks can be connected for communication, which realizes the multi network integration of perception layer of Internet of things
An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network
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Zhixiao Wang
2014-01-01
Full Text Available Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.
Research on social communication network evolution based on topology potential distribution
Zhao, Dongjie; Jiang, Jian; Li, Deyi; Zhang, Haisu; Chen, Guisheng
2011-12-01
Aiming at the problem of social communication network evolution, first, topology potential is introduced to measure the local influence among nodes in networks. Second, from the perspective of topology potential distribution the method of network evolution description based on topology potential distribution is presented, which takes the artificial intelligence with uncertainty as basic theory and local influence among nodes as essentiality. Then, a social communication network is constructed by enron email dataset, the method presented is used to analyze the characteristic of the social communication network evolution and some useful conclusions are got, implying that the method is effective, which shows that topology potential distribution can effectively describe the characteristic of sociology and detect the local changes in social communication network.
Chimeras in a network of three oscillator populations with varying network topology
DEFF Research Database (Denmark)
Martens, Erik Andreas
2010-01-01
this system as a model system, we discuss for the first time the influence of network topology on the existence of so-called chimera states. In this context, the network with three populations represents an interesting case because the populations may either be connected as a triangle, or as a chain, thereby......We study a network of three populations of coupled phase oscillators with identical frequencies. The populations interact nonlocally, in the sense that all oscillators are coupled to one another, but more weakly to those in neighboring populations than to those in their own population. Using...... representing the simplest discrete network of either a ring or a line segment of oscillator populations. We introduce a special parameter that allows us to study the effect of breaking the triangular network structure, and to vary the network symmetry continuously such that it becomes more and more chain...
Analysis of Broadcast Authentication Mechanism in Selected Network Topologies
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T. Vanek
2011-04-01
Full Text Available This paper deals with simulation of the broadcast authentication protocols using Colored Petri Nets and further optimizations in Matlab environment. Typical application of broadcast authentication protocols can be configurations where only one transmitter with multiple recipients exists (such as message exchange in sensor networks routing protocols, or the leader election process in sensors network. Authentication of every packet seems to be very effective way to mitigate an attack, however resulting in increase of end-to-end delay. To mitigate this drawback, the broadcast authentication protocols have been proposed. Concept of optimization of the broadcast authentication protocol DREAM parameters in a special case of fully N-ary tree and general random topology containing the same amount of nodes with regard to delay and energy consumption minimization is showed in the paper. Protocol DREAM was taken as an example of broadcast authenticating protocol to show how Color Petri Nets can be used to create a fully functional model of the protocol.
Conformal thermal tensor network and universal entropy on topological manifolds
Chen, Lei; Wang, Hao-Xin; Wang, Lei; Li, Wei
2017-11-01
Thermal quantum critical systems, with partition functions expressed as conformal tensor networks, are revealed to exhibit universal entropy corrections on nonorientable manifolds. Through high-precision tensor network simulations of several quantum chains, we identify the universal entropy SK=lnk on the Klein bottle, where k relates to quantum dimensions of the primary fields in conformal field theory (CFT). Different from the celebrated Affleck-Ludwig boundary entropy lng (g reflects noninteger ground-state degeneracy), SK has n o boundary dependence or surface energy terms accompanying it, and can be very conveniently extracted from thermal data. On the Möbius-strip manifold, we uncover an entropy SM=1/2 (lng +lnk ) in CFT, where 1/2 lng is associated with the only open edge of the Möbius strip and 1/2 lnk is associated with the nonorientable topology. As a useful application, we employ the universal entropy to accurately pinpoint the quantum phase transitions, even for those without local order parameters.
Laurito, Andres; The ATLAS collaboration
2017-01-01
Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...
Laurito, Andres; The ATLAS collaboration
2018-01-01
Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...
A Topology Potential-Based Method for Identifying Essential Proteins from PPI Networks.
Li, Min; Lu, Yu; Wang, Jianxin; Wu, Fang-Xiang; Pan, Yi
2015-01-01
Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.
Directory of Open Access Journals (Sweden)
Hui He
2013-01-01
Full Text Available It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.
Topology and robustness in the Drosophila segment polarity network.
Directory of Open Access Journals (Sweden)
Nicholas T Ingolia
2004-06-01
Full Text Available A complex hierarchy of genetic interactions converts a single-celled Drosophila melanogaster egg into a multicellular embryo with 14 segments. Previously, von Dassow et al. reported that a mathematical model of the genetic interactions that defined the polarity of segments (the segment polarity network was robust (von Dassow et al. 2000. As quantitative information about the system was unavailable, parameters were sampled randomly. A surprisingly large fraction of these parameter sets allowed the model to maintain and elaborate on the segment polarity pattern. This robustness is due to the positive feedback of gene products on their own expression, which induces individual cells in a model segment to adopt different stable expression states (bistability corresponding to different cell types in the segment polarity pattern. A positive feedback loop will only yield multiple stable states when the parameters that describe it satisfy a particular inequality. By testing which random parameter sets satisfy these inequalities, I show that bistability is necessary to form the segment polarity pattern and serves as a strong predictor of which parameter sets will succeed in forming the pattern. Although the original model was robust to parameter variation, it could not reproduce the observed effects of cell division on the pattern of gene expression. I present a modified version that incorporates recent experimental evidence and does successfully mimic the consequences of cell division. The behavior of this modified model can also be understood in terms of bistability in positive feedback of gene expression. I discuss how this topological property of networks provides robust pattern formation and how large changes in parameters can change the specific pattern produced by a network.
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WenJun Zhang
2014-06-01
Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.
Stability from Structure : Metabolic Networks Are Unlike Other Biological Networks
Van Nes, P.; Bellomo, D.; Reinders, M.J.T.; De Ridder, D.
2009-01-01
In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we
Zenil, Hector; Soler-Toscano, Fernando; Dingle, Kamaludin; Louis, Ard A.
2014-06-01
We show that numerical approximations of Kolmogorov complexity (K) of graphs and networks capture some group-theoretic and topological properties of empirical networks, ranging from metabolic to social networks, and of small synthetic networks that we have produced. That K and the size of the group of automorphisms of a graph are correlated opens up interesting connections to problems in computational geometry, and thus connects several measures and concepts from complexity science. We derive these results via two different Kolmogorov complexity approximation methods applied to the adjacency matrices of the graphs and networks. The methods used are the traditional lossless compression approach to Kolmogorov complexity, and a normalised version of a Block Decomposition Method (BDM) based on algorithmic probability theory.
Altered network topology in pediatric traumatic brain injury
Dennis, Emily L.; Rashid, Faisal; Babikian, Talin; Mink, Richard; Babbitt, Christopher; Johnson, Jeffrey; Giza, Christopher C.; Asarnow, Robert F.; Thompson, Paul M.
2017-11-01
Outcome after a traumatic brain injury (TBI) is quite variable, and this variability is not solely accounted for by severity or demographics. Identifying sub-groups of patients who recover faster or more fully will help researchers and clinicians understand sources of this variability, and hopefully lead to new therapies for patients with a more prolonged recovery profile. We have previously identified two subgroups within the pediatric TBI patient population with different recovery profiles based on an ERP-derived (event-related potential) measure of interhemispheric transfer time (IHTT). Here we examine structural network topology across both patient groups and healthy controls, focusing on the `rich-club' - the core of the network, marked by high degree nodes. These analyses were done at two points post-injury - 2-5 months (post-acute), and 13-19 months (chronic). In the post-acute time-point, we found that the TBI-slow group, those showing longitudinal degeneration, showed hyperconnectivity within the rich-club nodes relative to the healthy controls, at the expense of local connectivity. There were minimal differences between the healthy controls and the TBI-normal group (those patients who show signs of recovery). At the chronic phase, these disruptions were no longer significant, but closer analysis showed that this was likely due to the loss of power from a smaller sample size at the chronic time-point, rather than a sign of recovery. We have previously shown disruptions to white matter (WM) integrity that persist and progress over time in the TBI-slow group, and here we again find differences in the TBI-slow group that fail to resolve over the first year post-injury.
UPGRADE FOR HARDWARE/SOFTWARE SERVER AND NETWORK TOPOLOGY IN INFORMATION SYSTEMS
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Oleksii O. Kaplun
2011-02-01
Full Text Available The network modernization, educational information systems software and hardware updates problem is actual in modern term of information technologies prompt development. There are server applications and network topology of Institute of Information Technology and Learning Tools of National Academy of Pedagogical Sciences of Ukraine analysis and their improvement methods expound in the article. The article materials represent modernization results implemented to increase network efficiency and reliability, decrease response time in Institute’s network information systems. The article gives diagrams of network topology before upgrading and after finish of optimization and upgrading processes.
Regions, Networks and Fluids: Anaemia and Social Topology
Mol, A.M.; Law, John
1994-01-01
This is a paper about the topological presuppositions that frame the performance of social similarity and difference. It argues that 'the social' does not exist as a single spatial type, but rather performs itself in a recursive and topologically heterogeneous manner. Using material drawn from a
Chen, Qing; Zhang, Jinxiu; Hu, Ze
2017-02-23
This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites' relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime.
Uniqueness of self-similar solutions to the network flow in a given topological class
Trumper, Mariel Sáez
2008-01-01
In this paper we study the uniqueness of expanding self-similar solutions to the network flow in a fixed topological class. We prove the result via the parabolic Allen-Cahn approximation proved in \\cite{triodginz}. Moreover, we prove that any regular evolution of connected tree-like network (with an initial condition that might be not regular) is unique in a given a topological class.
Capacity Extension of Software Defined Data Center Networks With Jellyfish Topology
DEFF Research Database (Denmark)
Mehmeri, Victor; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso
We present a performance analysis of Jellyfish topology with Software-Defined commodity switches for Data Center networks. Our results show up to a 2-fold performance gain when compared to a Spanning Tree Protocol implementation.......We present a performance analysis of Jellyfish topology with Software-Defined commodity switches for Data Center networks. Our results show up to a 2-fold performance gain when compared to a Spanning Tree Protocol implementation....
Topological evolution of a metropolitan rail transport network : The case of Stockholm
Cats, O.
2017-01-01
The structure of transport networks is the outcome of a large number of infrastructure investment decisions taken over a long time span. Network indicators are widely used for characterizing transport network topology and its performance as well as provide insights on possible developments.
Energy-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks
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Xiaojuan Luo
2012-01-01
Full Text Available Based on the complex network theory, a new topological evolving model is proposed. In the evolution of the topology of sensor networks, the energy-aware mechanism is taken into account, and the phenomenon of change of the link and node in the network is discussed. Theoretical analysis and numerical simulation are conducted to explore the topology characteristics and network performance with different node energy distribution. We find that node energy distribution has the weak effect on the degree distribution P(k that evolves into the scale-free state, nodes with more energy carry more connections, and degree correlation is nontrivial disassortative. Moreover, the results show that, when nodes energy is more heterogeneous, the network is better clustered and enjoys higher performance in terms of the network efficiency and the average path length for transmitting data.
Scale-space measures for graph topology link protein network architecture to function.
Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen
2014-06-15
The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.
ReNoC: A Network-on-Chip Architecture with Reconfigurable Topology
DEFF Research Database (Denmark)
Stensgaard, Mikkel Bystrup; Sparsø, Jens
2008-01-01
This paper presents a Network-on-Chip (NoC) architecture that enables the network topology to be reconfigured. The architecture thus enables a generalized System-on-Chip (SoC) platform in which the topology can be customized for the application that is currently running on the chip, including long...... on physical circuit switching as found in FPGAs. The paper presents the ReNoC (Reconfigurable NoC) architecture and evaluates its potential. The evaluation design shows a 56% decrease in power consumption compared to a static 2D mesh topology....
Coudert, D; Ferreira, A; Muñoz, X
2000-06-10
Many results exist in the literature describing technological and theoretical advances in optical network topologies and design. However, an essential effort has yet to be made in linking those results together. We propose a step in this direction by giving optical layouts for several graph-theoretical topologies studied in the literature, using the optical transpose interconnection system (OTIS) architecture. These topologies include the family of partitioned optical passive star (POPS) and stack-Kautz networks as well as a generalization of the Kautz and the de Bruijn digraphs.
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Rian Fahrizal
2015-04-01
Full Text Available High speed computer networks with a large waiting time is a common from of network in the future. In this network are commonly used TCP algorithms have difficullty in sending data. There are several algorithms that has used the BIC, CUBIC and HTCP. These algorithms needs to be tested to determine its performance when apllied to the network topology with two dumbbells, and simple network. Teh results obtained testing the algorithms is best HTCP performance by having the smallest value.
Janssen, T W P; Hillebrand, A; Gouw, A; Geladé, K; Van Mourik, R; Maras, A; Oosterlaan, J
2017-11-01
Attention-deficit/hyperactivity disorder (ADHD) has been associated with widespread brain abnormalities in white and grey matter, affecting not only local, but global functional networks as well. In this study, we explored these functional networks using source-reconstructed electroencephalography in ADHD and typically developing (TD) children. We expected evidence for maturational delay, with underlying abnormalities in the default mode network. Electroencephalograms were recorded in ADHD (n=42) and TD (n=43) during rest, and functional connectivity (phase lag index) and graph (minimum spanning tree) parameters were derived. Dependent variables were global and local network metrics in theta, alpha and beta bands. We found evidence for a more centralized functional network in ADHD compared to TD children, with decreased diameter in the alpha band (η p 2 =0.06) and increased leaf fraction (η p 2 =0.11 and 0.08) in the alpha and beta bands, with underlying abnormalities in hub regions of the brain, including default mode network. The finding of a more centralized network is in line with maturational delay models of ADHD and should be replicated in longitudinal designs. This study contributes to the literature by combining high temporal and spatial resolution to construct EEG network topology, and associates maturational-delay and default-mode interference hypotheses of ADHD. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
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Bosiljka Tadić
Full Text Available Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener's concentration to the story, confirmed by self-rating, and closeness to the speaker's brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener's group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener's rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures for characterising functional brain networks under different stimuli.
Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site.
Aparicio, Sofía; Martínez-Garrido, María I; Ranz, Javier; Fort, Rafael; Izquierdo, Miguel Ángel G
2016-10-19
This paper provides a performance evaluation of tree and mesh routing topologies of wireless sensor networks (WSNs) in a cultural heritage site. The historical site selected was San Juan Bautista church in Talamanca de Jarama (Madrid, Spain). We report the preliminary analysis required to study the effects of heating in this historical location using WSNs to monitor the temperature and humidity conditions during periods of weeks. To test which routing topology was better for this kind of application, the WSNs were first deployed on the upper floor of the CAEND institute in Arganda del Rey simulating the church deployment, but in the former scenario there was no direct line of sight between the WSN elements. Two parameters were selected to evaluate the performance of the routing topologies of WSNs: the percentage of received messages and the lifetime of the wireless sensor network. To analyze in more detail which topology gave the best performance, other communication parameters were also measured. The tree topology used was the collection tree protocol and the mesh topology was the XMESH provided by MEMSIC (Andover, MA, USA). For the scenarios presented in this paper, it can be concluded that the tree topology lost fewer messages than the mesh topology.
Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site
Directory of Open Access Journals (Sweden)
Sofía Aparicio
2016-10-01
Full Text Available This paper provides a performance evaluation of tree and mesh routing topologies of wireless sensor networks (WSNs in a cultural heritage site. The historical site selected was San Juan Bautista church in Talamanca de Jarama (Madrid, Spain. We report the preliminary analysis required to study the effects of heating in this historical location using WSNs to monitor the temperature and humidity conditions during periods of weeks. To test which routing topology was better for this kind of application, the WSNs were first deployed on the upper floor of the CAEND institute in Arganda del Rey simulating the church deployment, but in the former scenario there was no direct line of sight between the WSN elements. Two parameters were selected to evaluate the performance of the routing topologies of WSNs: the percentage of received messages and the lifetime of the wireless sensor network. To analyze in more detail which topology gave the best performance, other communication parameters were also measured. The tree topology used was the collection tree protocol and the mesh topology was the XMESH provided by MEMSIC (Andover, MA, USA. For the scenarios presented in this paper, it can be concluded that the tree topology lost fewer messages than the mesh topology.
Estimating the size of the solution space of metabolic networks
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Mulet Roberto
2008-05-01
Full Text Available Abstract Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a
Toward a Robust Method of Presenting a Rich, Interconnected Deceptive Network Topology
2015-03-01
connections, and correspond to the Data Link and Network Layer of the Open Systems Interconnection ( OSI ) model . The OSI model is an abstraction that...research project to build a model for our network [39]. • Another option is to have a separate node in our network with a real, physical link that we...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS TOWARD A ROBUST METHOD OF PRESENTING A RICH, INTERCONNECTED DECEPTIVE NETWORK TOPOLOGY
Performance of Bus and Ring Network Topologies Based on SOA Bias Current
Rani, Aruna; Dewra, Sanjeev
2017-08-01
This paper investigates the number of nodes supported in bus and ring network topologies based on bias current of semiconductor optical amplifier (SOA) at 10 Gb/s. It is found that in bus topology, maximum 91 nodes are supported for 400 mA bias current of SOA at -30 dBm signal input power. In ring topology, more than 100 nodes are supported for 400 mA current of SOA. It is also found that for 100 mA current, 28 nodes are supported in bus topology but in ring topology maximum supported nodes are 39. The number of supported nodes increases with increase in bias current of SOA.
M-Polynomials and Topological Indices of Dominating David Derived Networks
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Kang Shin Min
2018-03-01
Full Text Available There is a strong relationship between the chemical characteristics of chemical compounds and their molecular structures. Topological indices are numerical values associated with the chemical molecular graphs that help to understand the physical features, chemical reactivity, and biological activity of chemical compound. Thus, the study of the topological indices is important. M-polynomial helps to recover many degree-based topological indices for example Zagreb indices, Randic index, symmetric division idex, inverse sum index etc. In this article we compute M-polynomials of dominating David derived networks of the first type, second type and third type of dimension n and find some topological properties by using these M-polynomials. The results are plotted using Maple to see the dependence of topological indices on the involved parameters.
Effect of network topology on the spreading of technologies
International Nuclear Information System (INIS)
Kocsis, G.; Kun, F.
2007-01-01
statistics. We showed that the topology of social contacts of agents plays a significant role in the spreading of telecommunication technologies. To make the model more realistic we considered networks of agents with small-world and scale-free properties. Based on computer simulations we showed that a complex system of a large number of local communities is more favorable for the spreading of technologies than a fully interconnected one
Hierarchical analysis of dependency in metabolic networks.
Gagneur, Julien; Jackson, David B; Casari, Georg
2003-05-22
Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html
Brain network analysis: separating cost from topology using cost-integration.
Directory of Open Access Journals (Sweden)
Cedric E Ginestet
Full Text Available A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i differences in weighted costs and (ii differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.
Brain network analysis: separating cost from topology using cost-integration.
Ginestet, Cedric E; Nichols, Thomas E; Bullmore, Ed T; Simmons, Andrew
2011-01-01
A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.
Scale-space measures for graph topology link protein network architecture to function
Hulsman, M.; Dimitrakopoulos, C.; De Ridder, J.
2014-01-01
MOTIVATION: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and
Debnath, Lokenath
2010-01-01
This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Konigsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real…
Flexible foraging shapes the topology of plant-pollinator interaction networks.
Spiesman, Brian J; Gratton, Claudio
2016-06-01
In plant-pollinator networks, foraging choices by pollinators help form the connecting links between species. Flexible foraging should therefore play an important role in defining network topology. Factors such as morphological trait complementarity limit a pollinator's pool of potential floral resources, but which potential resource species are actually utilized at a location depends on local environmental and ecological context. Pollinators can be highly flexible foragers, but the effect of this flexibility on network topology remains unclear. To understand how flexible foraging affects network topology, we examined differences between sets of locally realized interactions and corresponding sets of potential interactions within 25 weighted plant-pollinator networks in two different regions of the United States. We examined two possible mechanisms for flexible foraging effects on realized networks: (1) preferential targeting of higher-density plant resources, which should increase network nestedness, and (2) context-dependent resource partitioning driven by interspecific competition, which should increase modularity and complementary specialization. We found that flexible foraging has strong effects on realized network topology. Realized connectance was much lower than connectance based on potential interactions, indicating a local narrowing of diet breadth. Moreover, the foraging choices pollinators made, which particular plant species to visit and at what rates, resulted in networks that were significantly less nested and significantly more modular and specialized than their corresponding networks of potential interactions. Preferentially foraging on locally abundant resources was not a strong driver of the realization of potential interactions. However, the degree of modularity and complementary specialization both increased with the number of competing pollinator species and with niche availability. We therefore conclude that flexible foraging affects realized
Topological Traps Control Flow on Real Networks: The Case of Coordination Failures
Roca, Carlos P.; Lozano, Sergi; Arenas, Alex; Sánchez, Angel
2010-01-01
We study evolutionary games in real social networks, with a focus on coordination games. We find that populations fail to coordinate in the same behavior for a wide range of parameters, a novel phenomenon not observed in most artificial model networks. We show that this result arises from the relevance of correlations beyond the first neighborhood, in particular from topological traps formed by links between nodes of different degrees in regions with few or no redundant paths. This specificity of real networks has not been modeled so far with synthetic networks. We thus conclude that model networks must be improved to include these mesoscopic structures, in order to successfully address issues such as the emergence of cooperation in real societies. We finally show that topological traps are a very generic phenomenon that may arise in very many different networks and fields, such as opinion models, spread of diseases or ecological networks. PMID:21151565
Directory of Open Access Journals (Sweden)
Xiao-Hong Li
2014-01-01
Full Text Available Cooperative communication (CC is used in topology control as it can reduce the transmission power and expand the transmission range. However, all previous research on topology control under the CC model focused on maintaining network connectivity and minimizing the total energy consumption, which would lead to low network capacity, transmission interruption, or even network paralysis. Meanwhile, without considering the balance of energy consumption in the network, it would reduce the network lifetime and greatly affect the network performance. This paper tries to solve the above problems existing in the research on topology control under the CC model by proposing a power assignment (DCCPA algorithm based on dynamic cooperative clustering in cooperative ad hoc networks. The new algorithm clusters the network to maximize network capacity and makes the clusters communicate with each other by CC. To reduce the number of redundant links between clusters, we design a static clustering method by using Kruskal algorithm. To maximize the network lifetime, we also propose a cluster head rotating method which can reach a good tradeoff between residual energy and distance for the cluster head reselection. Experimental results show that DCCPA can improve 80% network capacity with Cooperative Bridges algorithm; meanwhile, it can improve 20% network lifetime.
Determination of keystone species in CSM food web: A topological analysis of network structure
LiQin Jiang; WenJun Zhang
2015-01-01
The importance of a species is correlated with its topological properties in a food web. Studies of keystone species provide the valuable theory and evidence for conservation ecology, biodiversity, habitat management, as well as the dynamics and stability of the ecosystem. Comparing with biological experiments, network methods based on topological structure possess particular advantage in the identification of keystone species. In present study, we quantified the relative importance of specie...
Optimization of communication network topology for navigation sharing among distributed satellites
Dang, Zhaohui; Zhang, Yulin
2013-01-01
Navigation sharing among distributed satellites is quite important for coordinated motion and collision avoidance. This paper proposes optimization methods of the communication network topology to achieve navigation sharing. The whole communication network constructing by inter-satellite links are considered as a topology graph. The aim of this paper is to find the communication network topology with minimum communication connections' number (MCCN) in different conditions. It has found that the communication capacity and the number of channels are two key parameters affecting the results. The model of MCCN topology for navigation sharing is established and corresponding method is designed. Two main scenarios, viz., homogeneous case and heterogeneous case, are considered. For the homogeneous case where each member has the same communication capacity, it designs a construction method (Algorithm 1) to find the MCCN topology. For the heterogeneous case, it introduces a modified genetic algorithm (Algorithm 2) to find the MCCN topology. When considering the fact that the number of channels is limited, the Algorithm 2 is further modified by adding a penalized term in the fitness function. The effectiveness of these algorithms is all proved in theoretical. Three examples are further tested to illustrate the methods developed in this paper.
Optimal flux patterns in cellular metabolic networks
Energy Technology Data Exchange (ETDEWEB)
Almaas, E
2007-01-20
The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.
Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks
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Giovanni Francesco Santonastaso
2018-01-01
Full Text Available Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, maximum flow entropy, link density and average path length have been evaluated for a set of 22 networks, both real and synthetic, with different size and topology. The obtained results led to identify suitable scaling laws of flow entropy and maximum flow entropy with water distribution network size, in the form of power–laws. The obtained relationships allow comparing the flow entropy of water distribution networks with different size, and provide an easy tool to define the maximum achievable entropy of a specific water distribution network. An example of application of the obtained relationships to the design of a water distribution network is provided, showing how, with a constrained multi-objective optimization procedure, a tradeoff between network cost and robustness is easily identified.
Multifractal analysis and topological properties of a new family of weighted Koch networks
Huang, Da-Wen; Yu, Zu-Guo; Anh, Vo
2017-03-01
Weighted complex networks, especially scale-free networks, which characterize real-life systems better than non-weighted networks, have attracted considerable interest in recent years. Studies on the multifractality of weighted complex networks are still to be undertaken. In this paper, inspired by the concepts of Koch networks and Koch island, we propose a new family of weighted Koch networks, and investigate their multifractal behavior and topological properties. We find some key topological properties of the new networks: their vertex cumulative strength has a power-law distribution; there is a power-law relationship between their topological degree and weight strength; the networks have a high weighted clustering coefficient of 0.41004 (which is independent of the scaling factor c) in the limit of large generation t; the second smallest eigenvalue μ2 and the maximum eigenvalue μn are approximated by quartic polynomials of the scaling factor c for the general Laplacian operator, while μ2 is approximately a quartic polynomial of c and μn= 1.5 for the normalized Laplacian operator. Then, we find that weighted koch networks are both fractal and multifractal, their fractal dimension is influenced by the scaling factor c. We also apply these analyses to six real-world networks, and find that the multifractality in three of them are strong.
Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies.
Mirzakhalili, Ehsan; Gourgou, Eleni; Booth, Victoria; Epureanu, Bogdan
2017-01-01
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.
Accepting Hybrid Networks of Evolutionary Processors with Special Topologies and Small Communication
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Jürgen Dassow
2010-08-01
Full Text Available Starting from the fact that complete Accepting Hybrid Networks of Evolutionary Processors allow much communication between the nodes and are far from network structures used in practice, we propose in this paper three network topologies that restrict the communication: star networks, ring networks, and grid networks. We show that ring-AHNEPs can simulate 2-tag systems, thus we deduce the existence of a universal ring-AHNEP. For star networks or grid networks, we show a more general result; that is, each recursively enumerable language can be accepted efficiently by a star- or grid-AHNEP. We also present bounds for the size of these star and grid networks. As a consequence we get that each recursively enumerable can be accepted by networks with at most 13 communication channels and by networks where each node communicates with at most three other nodes.
Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman
2016-01-01
Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies.
Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman
2016-01-01
Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies. PMID:27799906
Effect of the social influence on topological properties of user-object bipartite networks
Liu, Jian-Guo; Hu, Zhaolong; Guo, Qiang
2013-11-01
Social influence plays an important role in analyzing online users' collective behaviors [Salganik et al., Science 311, 854 (2006)]. However, the effect of the social influence from the viewpoint of theoretical model is missing. In this paper, by taking into account the social influence and users' preferences, we develop a theoretical model to analyze the topological properties of user-object bipartite networks, including the degree distribution, average nearest neighbor degree and the bipartite clustering coefficient, as well as topological properties of the original user-object networks and their unipartite projections. According to the users' preferences and the global ranking effect, we analyze the theoretical results for two benchmark data sets, Amazon and Bookcrossing, which are approximately consistent with the empirical results. This work suggests that this model is feasible to analyze topological properties of bipartite networks in terms of the social influence and the users' preferences.
Zhang, Ying; Chen, Wei; Liang, Jixing; Zheng, Bingxin; Jiang, Shengming
2015-12-01
It is expected that in the near future wireless sensor network (WSNs) will be more widely used in the mobile environment, in applications such as Autonomous Underwater Vehicles (AUVs) for marine monitoring and mobile robots for environmental investigation. The sensor nodes' mobility can easily cause changes to the structure of a network topology, and lead to the decline in the amount of transmitted data, excessive energy consumption, and lack of security. To solve these problems, a kind of efficient Topology Control algorithm for node Mobility (TCM) is proposed. In the topology construction stage, an efficient clustering algorithm is adopted, which supports sensor node movement. It can ensure the balance of clustering, and reduce the energy consumption. In the topology maintenance stage, the digital signature authentication based on Error Correction Code (ECC) and the communication mechanism of soft handover are adopted. After verifying the legal identity of the mobile nodes, secure communications can be established, and this can increase the amount of data transmitted. Compared to some existing schemes, the proposed scheme has significant advantages regarding network topology stability, amounts of data transferred, lifetime and safety performance of the network.
Modeling and dynamical topology properties of VANET based on complex networks theory
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Hong Zhang
2015-01-01
Full Text Available Vehicular Ad hoc Network (VANET is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I and Vehicle-to-Vehicle (V2V inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.
Jovanović, Stojan; Rotter, Stefan
2016-06-01
The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.
A specification-based IDS for detecting attacks on RPL-based network topology
Le, Anhtuan; Loo, Jonathan; Chai, Kok Keong; Aiash, Mahdi
2016-01-01
Routing Protocol for Low power and Lossy network (RPL) topology attacks can downgrade the network performance significantly by disrupting the optimal protocol structure. To detect such threats, we propose a RPL-specification, obtained by a semi-auto profiling technique that constructs a high-level abstract of operations through network simulation traces, to use as reference for verifying the node behaviors. This specification, including all the legitimate protocol states and transitions with ...
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.
Directory of Open Access Journals (Sweden)
Stojan Jovanović
2016-06-01
Full Text Available The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.
Santra, Tapesh; Kolch, Walter; Kholodenko, Boris N
2013-07-06
Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations. We propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells. The proposed algorithm
Modeling and dynamical topology properties of VANET based on complex networks theory
Zhang, Hong; Li, Jie
2015-01-01
Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What's more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a
Modeling and dynamical topology properties of VANET based on complex networks theory
Energy Technology Data Exchange (ETDEWEB)
Zhang, Hong; Li, Jie, E-mail: prof.li@foxmail.com [School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074 (China)
2015-01-15
Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a
Reconstruction of network topology using status-time-series data
Pandey, Pradumn Kumar; Badarla, Venkataramana
2018-01-01
Uncovering the heterogeneous connection pattern of a networked system from the available status-time-series (STS) data of a dynamical process on the network is of great interest in network science and known as a reverse engineering problem. Dynamical processes on a network are affected by the structure of the network. The dependency between the diffusion dynamics and structure of the network can be utilized to retrieve the connection pattern from the diffusion data. Information of the network structure can help to devise the control of dynamics on the network. In this paper, we consider the problem of network reconstruction from the available status-time-series (STS) data using matrix analysis. The proposed method of network reconstruction from the STS data is tested successfully under susceptible-infected-susceptible (SIS) diffusion dynamics on real-world and computer-generated benchmark networks. High accuracy and efficiency of the proposed reconstruction procedure from the status-time-series data define the novelty of the method. Our proposed method outperforms compressed sensing theory (CST) based method of network reconstruction using STS data. Further, the same procedure of network reconstruction is applied to the weighted networks. The ordering of the edges in the weighted networks is identified with high accuracy.
Co-regulation of metabolic genes is better explained by flux coupling than by network distance.
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Richard A Notebaart
2008-01-01
Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.
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Qiu eXiangzhe
2016-05-01
Full Text Available Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM. However, the DM-related changes in the topological properties in functional brain networks are almost unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs, followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized shortest path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing the functional evidence for the abnormalities of brain networks in DM.
Topological probability and connection strength induced activity in complex neural networks
International Nuclear Information System (INIS)
Du-Qu, Wei; Bo, Zhang; Dong-Yuan, Qiu; Xiao-Shu, Luo
2010-01-01
Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied. (general)
Topology control of tactical wireless sensor networks using energy efficient zone routing
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Preetha Thulasiraman
2016-02-01
Full Text Available The US Department of Defense (DoD routinely uses wireless sensor networks (WSNs for military tactical communications. Sensor node die-out has a significant impact on the topology of a tactical WSN. This is problematic for military applications where situational data is critical to tactical decision making. To increase the amount of time all sensor nodes remain active within the network and to control the network topology tactically, energy efficient routing mechanisms must be employed. In this paper, we aim to provide realistic insights on the practical advantages and disadvantages of using established routing techniques for tactical WSNs. We investigate the following established routing algorithms: direct routing, minimum transmission energy (MTE, Low Energy Adaptive Cluster Head routing (LEACH, and zone clustering. Based on the node die out statistics observed with these algorithms and the topological impact the node die outs have on the network, we develop a novel, energy efficient zone clustering algorithm called EZone. Via extensive simulations using MATLAB, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EZone algorithm tactically controls the topology of the network, thereby maintaining significant service area coverage when compared to the other routing algorithms.
Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network
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Barabási Albert-László
2004-01-01
Full Text Available Abstract Background Transcriptional regulation of cellular functions is carried out through a complex network of interactions among transcription factors and the promoter regions of genes and operons regulated by them.To better understand the system-level function of such networks simplification of their architecture was previously achieved by identifying the motifs present in the network, which are small, overrepresented, topologically distinct regulatory interaction patterns (subgraphs. However, the interaction of such motifs with each other, and their form of integration into the full network has not been previously examined. Results By studying the transcriptional regulatory network of the bacterium, Escherichia coli, we demonstrate that the two previously identified motif types in the network (i.e., feed-forward loops and bi-fan motifs do not exist in isolation, but rather aggregate into homologous motif clusters that largely overlap with known biological functions. Moreover, these clusters further coalesce into a supercluster, thus establishing distinct topological hierarchies that show global statistical properties similar to the whole network. Targeted removal of motif links disintegrates the network into small, isolated clusters, while random disruptions of equal number of links do not cause such an effect. Conclusion Individual motifs aggregate into homologous motif clusters and a supercluster forming the backbone of the E. coli transcriptional regulatory network and play a central role in defining its global topological organization.
A cluster-based architecture to structure the topology of parallel wireless sensor networks.
Lloret, Jaime; Garcia, Miguel; Bri, Diana; Diaz, Juan R
2009-01-01
A wireless sensor network is a self-configuring network of mobile nodes connected by wireless links where the nodes have limited capacity and energy. In many cases, the application environment requires the design of an exclusive network topology for a particular case. Cluster-based network developments and proposals in existence have been designed to build a network for just one type of node, where all nodes can communicate with any other nodes in their coverage area. Let us suppose a set of clusters of sensor nodes where each cluster is formed by different types of nodes (e.g., they could be classified by the sensed parameter using different transmitting interfaces, by the node profile or by the type of device: laptops, PDAs, sensor etc.) and exclusive networks, as virtual networks, are needed with the same type of sensed data, or the same type of devices, or even the same type of profiles. In this paper, we propose an algorithm that is able to structure the topology of different wireless sensor networks to coexist in the same environment. It allows control and management of the topology of each network. The architecture operation and the protocol messages will be described. Measurements from a real test-bench will show that the designed protocol has low bandwidth consumption and also demonstrates the viability and the scalability of the proposed architecture. Our ccluster-based algorithm is compared with other algorithms reported in the literature in terms of architecture and protocol measurements.
Topology of Innovation Spaces in the Knowledge Networks Emerging through Questions-And-Answers.
Andjelković, Miroslav; Tadić, Bosiljka; Mitrović Dankulov, Marija; Rajković, Milan; Melnik, Roderick
2016-01-01
The communication processes of knowledge creation represent a particular class of human dynamics where the expertise of individuals plays a substantial role, thus offering a unique possibility to study the structure of knowledge networks from online data. Here, we use the empirical evidence from questions-and-answers in mathematics to analyse the emergence of the network of knowledge contents (or tags) as the individual experts use them in the process. After removing extra edges from the network-associated graph, we apply the methods of algebraic topology of graphs to examine the structure of higher-order combinatorial spaces in networks for four consecutive time intervals. We find that the ranking distributions of the suitably scaled topological dimensions of nodes fall into a unique curve for all time intervals and filtering levels, suggesting a robust architecture of knowledge networks. Moreover, these networks preserve the logical structure of knowledge within emergent communities of nodes, labeled according to a standard mathematical classification scheme. Further, we investigate the appearance of new contents over time and their innovative combinations, which expand the knowledge network. In each network, we identify an innovation channel as a subgraph of triangles and larger simplices to which new tags attach. Our results show that the increasing topological complexity of the innovation channels contributes to network's architecture over different time periods, and is consistent with temporal correlations of the occurrence of new tags. The methodology applies to a wide class of data with the suitable temporal resolution and clearly identified knowledge-content units.
Al Tarhuni, Mohammed; Goldman, Daniel; Jackson, Dwayne N
2016-08-01
To provide detailed geometric and topological descriptions of the rat gluteus maximus arteriolar network, and to measure the distribution of diameters and lengths as well as their associated variability within and between networks. Complete arteriolar networks arising from feed artery (inferior gluteal artery) to terminal branches were imaged under baseline conditions, using IVVM. Photomontages of complete networks were assembled and evaluated offline for measurements of geometry and topology. Single-line (skeletonized) tracings of the networks were made for fractal analysis. Diameters and lengths decreased with increasing topological order (centrifugal), while number of elements increased with increasing order. Horton's laws were shown to be valid within the arteriolar networks of the rat GM. Inter-network variability in diameter (~5-22%) and length (~17-30%) at each order was generally lower than the corresponding intra-network variability in diameter (~10-48%) and length (~39-106%). Data presented in this study provide crucial quantitative analysis of complete arteriolar networks within healthy skeletal muscle, and may serve as ideal experimental inputs for future theoretical studies of skeletal muscle microvascular structure and function. © 2016 John Wiley & Sons Ltd.
Morine, Melissa J; Gu, Hong; Myers, Ransom A; Bielawski, Joseph P
2009-05-01
The relation between structure and function in biologic networks is a central point of systems biology research. Key functional features--notably, efficiency and robustness--are linked to the topologic structure of a network, and there appears to be a degree of trade-off between these features, i.e., simulation studies indicate that more efficient networks tend to be less robust. Here, we investigate this issue in metabolic networks from 105 lineages of bacteria having a wide range of ecologies. We take quantitative measurements on each network and integrate this network data with ecologic data using a phylogenetic comparative model. In this setting, we find that biologic conclusions obtained with classical phylogenetic comparative methods are sensitive to correlations between model covariates and phylogenetic branch length. To avoid this problem, we propose a revised statistical framework--hierarchical mixed-effect regression--to accommodate phylogenetic nonindependence. Using this approach, we show that the cartography of metabolic networks does indeed reflect a trade-off between efficiency and robustness. Furthermore, ecologic characteristics related to niche breadth are strong predictors of network shape. Given the broad variation in niche breadth seen among species, we predict that there is no universally optimal balance between efficiency and robustness in bacterial metabolic networks and, thus, no universally optimal network structure. These results highlight the biologic relevance of variation in network structure and the potential role of niche breadth in shaping metabolic strategies of efficiency and robustness.
Hayat, Sakander
2017-11-01
Structure-based topological descriptors/indices of complex chemical networks enable prediction of physico-chemical properties and the bioactivities of these compounds through QSAR/QSPR methods. In this paper, we have developed a rigorous computational and theoretical technique to compute various distance-based topological indices of complex chemical networks. A fullerene is called the IPR (Isolated-Pentagon-Rule) fullerene, if every pentagon in it is surrounded by hexagons only. To ensure the applicability of our technique, we compute certain distance-based indices of an infinite family of IPR fullerenes. Our results show that the proposed technique is more diverse and bears less algorithmic and combinatorial complexity.
Impact of the environment and the topology on the performance of hierarchical body area networks
Directory of Open Access Journals (Sweden)
Ferrari Gianluigi
2011-01-01
Full Text Available Abstract Personal area networks and, more specifically, body area networks (BANs are key building blocks of future generation networks and of the Internet of Things as well. In this article, we present a novel analytical framework for network performance analysis of body sensor networks with hierarchical (tree topologies. This framework takes into account the specificities of the on-body channel modeling and the impact of the surrounding environment. The obtained results clearly highlight the differences between indoor and outdoor scenarios, and provide several insights on BAN design and analysis. In particular, it will be shown that the BAN topology should be selected according to the foreseen medical application and the deployment environment.
Study on the evolutionary optimization of the topology of network control systems
DEFF Research Database (Denmark)
Zhou, Z.; Chen, B.; Wang, H.
2010-01-01
Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology...... optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition...
Topological Analysis and Measurements of an Online Chinese Student Social Network
Sun, Duoyong; Wu, Jiang; Zheng, Shenghua; Hu, Bin; Carley, Kathleen M.
Online social network attracts more researchers now. In this paper, we topologically analyze an online Chinese student social network-Xiaonei.com. We use Python language to crawl two datasets of Xiaonei in January and February, 2008. The degree distribution and small world phenomena are testified. We also use a social network analysis tool to analyze these two datasets from the viewpoint of social network structure. Seventeen measurements such as Fragmentation, Component Count, Strong/Weak are summarized to identify the exogenous attributes of Xiaonei.com. Additionally, two latent applications of online social network service are proposed in the discussion section.
Understanding Terrorist Network Topologies and Their Resilience Against Disruption
Lindelauf, R.; Borm, P.E.M.; Hamers, H.J.M.
2009-01-01
This article investigates the structural position of covert (terrorist or criminal) networks. Using the secrecy versus information tradeoff characterization of covert networks it is shown that their network structures are generally not small-worlds, in contradistinction to many overt social
Topology dependent epidemic spreading velocity in weighted networks
Duan, W.; Quax, R.; Lees, M.; Qiu, X.; Sloot, P.M.A.
2014-01-01
Many diffusive processes occur on structured networks with weighted links, such as disease spread by airplane transport or information diffusion in social networks or blogs. Understanding the impact of weight-connectivity correlations on epidemic spreading in weighted networks is crucial to support
Analysis on the urban street network of Korea: Connections between topology and meta-information
Lee, Byoung-Hwa; Jung, Woo-Sung
2018-05-01
Cities consist of infrastructure that enables transportation, which can be considered as topology in abstract terms. Once cities are physically organized in terms of infrastructure, people interact with each other to form the values, which can be regarded as the meta-information of the cities. The topology and meta-information coevolve together as the cities are developed. In this study, we investigate the relationship between the topology and meta-information for a street network, which has aspects of both a complex network and planar graph. The degree of organization of a street structure determines the efficiency and productivity of the city in that they act as blood vessels to transport people, goods, and information. We analyze the topological aspect of a street network using centralities including the betweenness, closeness, straightness, and information. We classify the cities into several groups that share common meta-information based on the centrality, indicating that the topological factor of the street structure is closely related to meta-information through coevolution. We also obtain the coevolution in the planned cities using the regularity. Another footprint is the relation between the street segment length and the population, which shows the sublinear scaling.
International Nuclear Information System (INIS)
Chen, Hua-Jun; Shi, Hai-Bin; Jiang, Long-Feng; Li, Lan; Chen, Rong
2018-01-01
To investigate structural brain connectome alterations in cirrhotic patients with prior overt hepatic encephalopathy (OHE). Seventeen cirrhotic patients with prior OHE (prior-OHE), 18 cirrhotic patients without prior OHE (non-prior-OHE) and 18 healthy controls (HC) underwent diffusion tensor imaging. Neurocognitive functioning was assessed with Psychometric Hepatic Encephalopathy Score (PHES). Using a probabilistic fibre tracking approach, we depicted the whole-brain structural network as a connectivity matrix of 90 regions (derived from the Automated Anatomic Labeling atlas). Graph theory-based analyses were performed to analyse topological properties of the brain network. The analysis of variance showed significant group effects on several topological properties, including network strength, global efficiency and local efficiency. A progressive decrease trend for these metrics was found from non-prior-OHE to prior-OHE, compared with HC. Among the three groups, the regions with altered nodal efficiency were mainly distributed in the frontal and occipital cortices, paralimbic system and subcortical regions. The topological metrics, such as network strength and global efficiency, were correlated with PHES among cirrhotic patients. The cirrhotic patients developed structural brain connectome alterations; this is aggravated by prior OHE episode. Disrupted topological organization of the brain structural network may account for cognitive impairments related to prior OHE. (orig.)
The shortest path problem in the stochastic networks with unstable topology.
Shirdel, Gholam H; Abdolhosseinzadeh, Mohsen
2016-01-01
The stochastic shortest path length is defined as the arrival probability from a given source node to a given destination node in the stochastic networks. We consider the topological changes and their effects on the arrival probability in directed acyclic networks. There is a stable topology which shows the physical connections of nodes; however, the communication between nodes does not stable and that is defined as the unstable topology where arcs may be congested. A discrete time Markov chain with an absorbing state is established in the network according to the unstable topological changes. Then, the arrival probability to the destination node from the source node in the network is computed as the multi-step transition probability of the absorption in the final state of the established Markov chain. It is assumed to have some wait states, whenever there is a physical connection but it is not possible to communicate between nodes immediately. The proposed method is illustrated by different numerical examples, and the results can be used to anticipate the probable congestion along some critical arcs in the delay sensitive networks.
Energy Technology Data Exchange (ETDEWEB)
Chen, Hua-Jun [Fujian Medical University Union Hospital, Department of Radiology, Fuzhou (China); The First Affiliated Hospital of Nanjing Medical University, Department of Radiology, Nanjing (China); Shi, Hai-Bin [The First Affiliated Hospital of Nanjing Medical University, Department of Radiology, Nanjing (China); Jiang, Long-Feng [The First Affiliated Hospital of Nanjing Medical University, Department of Infectious Diseases, Nanjing (China); Li, Lan [Fujian Medical University Union Hospital, Department of Radiology, Fuzhou (China); Chen, Rong [University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, Baltimore, MD (United States); Beijing Institute of Technology, Advanced Innovation Center for Intelligent Robots and Systems, Beijing (China)
2018-01-15
To investigate structural brain connectome alterations in cirrhotic patients with prior overt hepatic encephalopathy (OHE). Seventeen cirrhotic patients with prior OHE (prior-OHE), 18 cirrhotic patients without prior OHE (non-prior-OHE) and 18 healthy controls (HC) underwent diffusion tensor imaging. Neurocognitive functioning was assessed with Psychometric Hepatic Encephalopathy Score (PHES). Using a probabilistic fibre tracking approach, we depicted the whole-brain structural network as a connectivity matrix of 90 regions (derived from the Automated Anatomic Labeling atlas). Graph theory-based analyses were performed to analyse topological properties of the brain network. The analysis of variance showed significant group effects on several topological properties, including network strength, global efficiency and local efficiency. A progressive decrease trend for these metrics was found from non-prior-OHE to prior-OHE, compared with HC. Among the three groups, the regions with altered nodal efficiency were mainly distributed in the frontal and occipital cortices, paralimbic system and subcortical regions. The topological metrics, such as network strength and global efficiency, were correlated with PHES among cirrhotic patients. The cirrhotic patients developed structural brain connectome alterations; this is aggravated by prior OHE episode. Disrupted topological organization of the brain structural network may account for cognitive impairments related to prior OHE. (orig.)
Directory of Open Access Journals (Sweden)
Wingender Edgar
2008-05-01
Full Text Available Abstract Background Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine 'general' with 'particular' properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter – the pairwise disconnectivity index of a network's element – that is capable of such bridging. Results The pairwise disconnectivity index quantifies how crucial an individual element is for sustaining the communication ability between connected pairs of vertices in a network that is displayed as a directed graph. Such an element might be a vertex (i.e., molecules, genes, an edge (i.e., reactions, interactions, as well as a group of vertices and/or edges. The index can be viewed as a measure of topological redundancy of regulatory paths which connect different parts of a given network and as a measure of sensitivity (robustness of this network to the presence (absence of each individual element. Accordingly, we introduce the notion of a path-degree of a vertex in terms of its corresponding incoming, outgoing and mediated paths, respectively. The pairwise disconnectivity index has been applied to the analysis of several regulatory networks from various organisms. The importance of an individual vertex or edge for the coherence of the network is determined by the particular position of the given element in the whole network. Conclusion Our approach enables to evaluate the effect of removing each element (i.e., vertex, edge, or their combinations from a network. The greatest potential value of
Directory of Open Access Journals (Sweden)
Pengfei Guo
2014-01-01
Full Text Available This paper deals with the fault detection problem for a class of discrete-time wireless networked control systems described by switching topology with uncertainties and disturbances. System states of each individual node are affected not only by its own measurements, but also by other nodes’ measurements according to a certain network topology. As the topology of system can be switched in a stochastic way, we aim to design H∞ fault detection observers for nodes in the dynamic time-delay systems. By using the Lyapunov method and stochastic analysis techniques, sufficient conditions are acquired to guarantee the existence of the filters satisfying the H∞ performance constraint, and observer gains are derived by solving linear matrix inequalities. Finally, an illustrated example is provided to verify the effectiveness of the theoretical results.
Synchronization and Control of Halo-Chaos in Beam Transport Network with Small World Topology
International Nuclear Information System (INIS)
Liu Qiang; Fang Jinqing; Li Yong
2007-01-01
The synchronous conditions of two kinds of the small-world (SW) network are studied. The small world topology can affect on dynamical behaviors of the beam transport network (BTN) largely, if the BTN is constructed with the SW topology, the global linear coupling and special linear feedback can realize the synchronization control of beam halo-chaos as well as periodic state in the BTN with the SW topology, respectively. This important result can provide an effective way for the experimental study and the engineering design of the BTN in the high-current accelerator driven radioactive clean nuclear power systems, and may have potential use in prospective applications for halo-chaos secure communication.
Topological Control on the Structural Relaxation of Atomic Networks under Stress
Bauchy, Mathieu; Wang, Mengyi; Yu, Yingtian; Wang, Bu; Krishnan, N. M. Anoop; Masoero, Enrico; Ulm, Franz-Joseph; Pellenq, Roland
2017-07-01
Upon loading, atomic networks can feature delayed irreversible relaxation. However, the effect of composition and structure on relaxation remains poorly understood. Herein, relying on accelerated molecular dynamics simulations and topological constraint theory, we investigate the relationship between atomic topology and stress-induced structural relaxation, by taking the example of creep deformations in calcium silicate hydrates (C - S - H ), the binding phase of concrete. Under constant shear stress, C - S - H is found to feature delayed logarithmic shear deformations. We demonstrate that the propensity for relaxation is minimum for isostatic atomic networks, which are characterized by the simultaneous absence of floppy internal modes of relaxation and eigenstress. This suggests that topological nanoengineering could lead to the discovery of nonaging materials.
Directory of Open Access Journals (Sweden)
Ning Li
2017-05-01
Full Text Available The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs project is to make autonomous underwater vehicles (AUVs, remote operated vehicles (ROVs and unmanned surface vehicles (USVs more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the
Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan
2017-05-04
The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power
Yang, Q; Siganos, G; Faloutsos, M; Lonardi, S
2006-01-01
Recent research efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the system biology community. In this study, we attempt to understand better the topological structure of PPI networks by comparing them against man-made communication networks, and more specifically, the Internet. Our comparative study is based on a comprehensive set of graph metrics. Our results exhibit an interesting dichotomy. On the one hand, both networks share several macroscopic properties such as scale-free and small-world properties. On the other hand, the two networks exhibit significant topological differences, such as the cliqueishness of the highest degree nodes. We attribute these differences to the distinct design principles and constraints that both networks are assumed to satisfy. We speculate that the evolutionary constraints that favor the survivability and diversification are behind the building process of PPI networks, whereas the leading force in shaping the Internet topology is a decentralized optimization process geared towards efficient node communication.
On Functional Module Detection in Metabolic Networks
Koch, Ina; Ackermann, Jörg
2013-01-01
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145
Topology effects on nonaffine behavior of semiflexible fiber networks
Hatami-Marbini, H.; Shriyan, V.
2017-12-01
Filamentous semiflexible networks define the mechanical and physical properties of many materials such as cytoskeleton. In the absence of a distinct unit cell, the Mikado fiber network model is commonly used algorithm for representing the microstructure of these networks in numerical models. Nevertheless, certain types of filamentous structures such as collagenous tissues, at early stages of their development, are assembled by growth of individual fibers from random nucleation sites. In this work, we develop a computational model to investigate the mechanical response of such networks by characterizing their nonaffine behavior. We show that the deformation of these networks is nonaffine at all length scales. Furthermore, similar to Mikado networks, the degree of nonaffinity in these structures decreases with increasing the probing length scale, the network fiber density, and/or the bending stiffness of constituting filaments. Nevertheless, despite the lower coordination number of these networks, their deformation field is more affine than that of the Mikado networks with the same fiber density and fiber mechanical properties.
Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alexander S.
2015-01-01
Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles. PMID:25826650
Directory of Open Access Journals (Sweden)
Hyun-Seob Song
2015-03-01
Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.
International Nuclear Information System (INIS)
Yang Dong-Sheng; Liu Zhen-Wei; Liu Zhao-Bing; Zhao Yan
2012-01-01
The networked synchronization problem of a class of master-slave chaotic systems with time-varying communication topologies is investigated in this paper. Based on algebraic graph theory and matrix theory, a simple linear state feedback controller is designed to synchronize the master chaotic system and the slave chaotic systems with a time-varying communication topology connection. The exponential stability of the closed-loop networked synchronization error system is guaranteed by applying Lyapunov stability theory. The derived novel criteria are in the form of linear matrix inequalities (LMIs), which are easy to examine and tremendously reduce the computation burden from the feedback matrices. This paper provides an alternative networked secure communication scheme which can be extended conveniently. An illustrative example is given to demonstrate the effectiveness of the proposed networked synchronization method. (general)
DEFF Research Database (Denmark)
Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens
2008-01-01
is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...... Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes. Conclusion: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks...
Topological data analysis of contagion maps for examining spreading processes on networks
Taylor, Dane; Klimm, Florian; Harrington, Heather A.; Kramár, Miroslav; Mischaikow, Konstantin; Porter, Mason A.; Mucha, Peter J.
2015-01-01
Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth’s surface; however, in modern contagions long-range edges—for example, due to airline transportation or communication media—allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct “contagion maps” that use multiple contagions on a network to map the nodes as a point cloud. By analyzing the topology, geometry, and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modeling, forecast, and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks. PMID:26194875
Exploitation of genetic interaction network topology for the prediction of epistatic behavior
Alanis Lobato, Gregorio
2013-10-01
Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.
Topological Origin of the Network Dilation Anomaly in Ion-Exchanged Glasses
Wang, Mengyi; Smedskjaer, Morten M.; Mauro, John C.; Sant, Gaurav; Bauchy, Mathieu
2017-11-01
Ion exchange is commonly used to strengthen oxide glasses. However, the resulting stuffed glasses usually do not reach the molar volume of as-melted glasses of similar composition—a phenomenon known as the network dilation anomaly. This behavior seriously limits the potential for the chemical strengthening of glasses and its origin remains one of the mysteries of glass science. Here, based on molecular dynamics simulations of sodium silicate glasses coupled with topological constraint theory, we show that the topology of the atomic network controls the extent of ion-exchange-induced dilation. We demonstrate that isostatic glasses do not show any network dilation anomaly. This is found to arise from the combined absence of floppy modes of deformation and internal eigenstress in isostatic atomic networks.
Topological data analysis of contagion maps for examining spreading processes on networks
Taylor, Dane
2015-07-21
Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth\\'s surface; however, in modern contagions long-range edges - for example, due to airline transportation or communication media - allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct \\'contagion maps\\' that use multiple contagions on a network to map the nodes as a point cloud. By analysing the topology, geometry and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modelling, forecast and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.
Small Strain Topological Effects of Biopolymer Networks with Rigid Cross-Links
Zagar, G.; Onck, P. R.; Van der Giessen, E.; Garikipati, K; Arruda, EM
2010-01-01
Networks of cross-linked filamentous biopolymers form topological structures characterized by L, T and X cross-link types of connectivity 2, 3 and 4, respectively. The distribution of cross-links over these three types proofs to be very important for the initial elastic shear stiffness of isotropic
High-capacity photorefractive neural network implementing a kohonen topological map.
Frauel, Y; Pauliat, G; Evilling, A; Roosen, G
2001-10-10
We designed and built a high-capacity neural network based on volume holographic interconnections in a photorefractive crystal. We used this system to implement a Kohonen topological map. We describe and justify our optical setup and present some experimental results of self-organization in the learning database.
Review of Network Topologies and Protection Principles in Marine and Offshore Applications
DEFF Research Database (Denmark)
Ciontea, Catalin-Iosif; Bak, Claus Leth; Blaabjerg, Frede
2015-01-01
challenges. As result, development of new protection techniques that can offer improved functionalities compared to the actual solutions for marine and offshore applications is needed. This paper reviews the network topologies in such applications and presents the requirements of a system able to protect...
High-Capacity Photorefractive Neural Network Implementing a Kohonen Topological Map
Frauel, Yann; Pauliat, Gilles; Villing, André; Roosen, Gérald
2001-10-01
We designed and built a high-capacity neural network based on volume holographic interconnections in a photorefractive crystal. We used this system to implement a Kohonen topological map. We describe and justify our optical setup and present some experimental results of self-organization in the learning database.
Directory of Open Access Journals (Sweden)
Qing Chen
2017-02-01
Full Text Available This article investigates the dynamic topology control problemof satellite cluster networks (SCNs in Earth observation (EO missions by applying a novel metric of stability for inter-satellite links (ISLs. The properties of the periodicity and predictability of satellites’ relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime.
A Dynamic Game on Network Topology for Counterinsurgency Applications
2015-03-26
41 9 Trusted connections within the 11 September, 2001 Hijacker Network ( Krebs ...2002) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 10 Comparison of the 11 September, 2001 Hijacker Network ( Krebs , 2002...operations. Military commanders employ their resources through a targeting cycle to effectively select and prioritize targets and match them to appropriate
Fibril growth kinetics link buffer conditions and topology of 3D collagen I networks.
Kalbitzer, Liv; Pompe, Tilo
2018-02-01
Three-dimensional fibrillar networks reconstituted from collagen I are widely used as biomimetic scaffolds for in vitro and in vivo cell studies. Various physicochemical parameters of buffer conditions for in vitro fibril formation are well known, including pH-value, ion concentrations and temperature. However, there is a lack of a detailed understanding of reconstituting well-defined 3D network topologies, which is required to mimic specific properties of the native extracellular matrix. We screened a wide range of relevant physicochemical buffer conditions and characterized the topology of the reconstituted 3D networks in terms of mean pore size and fibril diameter. A congruent analysis of fibril formation kinetics by turbidimetry revealed the adjustment of the lateral growth phase of fibrils by buffer conditions to be key in the determination of pore size and fibril diameter of the networks. Although the kinetics of nucleation and linear growth phase were affected by buffer conditions as well, network topology was independent of those two growth phases. Overall, the results of our study provide necessary insights into how to engineer 3D collagen matrices with an independent control over topology parameters, in order to mimic in vivo tissues in in vitro experiments and tissue engineering applications. The study reports a comprehensive analysis of physicochemical conditions of buffer solutions to reconstitute defined 3D collagen I matrices. By a combined analysis of network topology, i.e., pore size and fibril diameter, and the kinetics of fibril formation we can reveal the dependence of 3D network topology on buffer conditions, such as pH-value, phosphate concentration and sodium chloride content. With those results we are now able to provide engineering strategies to independently tune the topology parameters of widely used 3D collagen scaffolds based on the buffer conditions. By that, we enable the straightforward mimicking of extracellular matrices of in vivo
Cannistraci, Carlo
2013-06-21
Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The
Dynamics in small worlds of tree topologies of wireless sensor networks
DEFF Research Database (Denmark)
Li, Qiao; Zhang, Baihai; Fan, Zhun
2012-01-01
Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients, are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link additions, characteristic path lengths reduce...... rapidly and clustering coefficients increase greatly. A tree abstract, Cayley tree, is considered for the study of the navigation algorithm, which runs automatically in the small worlds of tree-based networks. In the further study, epidemics in the small worlds of tree-based wireless sensor networks...
Impact of Network Coding on Delay and Throughput in Practical Wireless Chain Topologies
DEFF Research Database (Denmark)
Hundebøll, Martin; Rein, Stephan Alexander; Fitzek, Frank
2013-01-01
In this paper, we present results from a practical evaluation of network coding in a setup consisting of eight nodes deployed in a chain topology. With the tradition pure relaying, delay increases dramatically as the network gets congested, and here network coding helps to moderate this increase...... in delay, as well improving throughput. The practical evaluation shows that network coding provides up to a five-fold decrease in delay, while retaining the expected gain in throughput. To address an unecessary delay when using network coding in low-load scenarios, we propose and evaluate a scheme...... for adaptive buffering. With this, we show that the benefits from pure relaying can be combined with the improved performance from network coding. The software used to apply network coding and evaluate this in a practical network is made publicly available for further research and tests....
Backoff-stage synchronization in three-hop string-topology wireless networks with hidden nodes
Sanada, Kosuke; Sekiya, Hiroo; Komuro, Nobuyoshi; Sakata, Shiro
In IEEE 802.11 wireless multi-hop networks, each node works individually and their individual operations generate entire network dynamics. It is important to clarify the network dynamics in wireless multi-hop networks for designing and constructing multi-hop communication networks. This paper presents the network-dynamics investigations for three-hop string-topology wireless network in detail. From the investigations, a “backoff-stage synchronization” phenomenon, which is mutuality between hidden nodes, is found. The mechanism of the backoff-stage synchronization is expressed and the sufficient conditions for the synchronization occurrence are given. This phenomenon gives some impacts on the IEEE 802.11 multi-hop-network communications.
Topology determines force distributions in one-dimensional random spring networks
Heidemann, Knut M.; Sageman-Furnas, Andrew O.; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F.; Wardetzky, Max
2018-02-01
Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load. Rupture occurs when fibers break under excessive force and when that failure propagates. Therefore, it is crucial to understand force distributions. Force distributions within such networks are typically highly inhomogeneous and are not well understood. Here we construct a simple one-dimensional model system with periodic boundary conditions by randomly placing linear springs on a circle. We consider ensembles of such networks that consist of N nodes and have an average degree of connectivity z but vary in topology. Using a graph-theoretical approach that accounts for the full topology of each network in the ensemble, we show that, surprisingly, the force distributions can be fully characterized in terms of the parameters (N ,z ) . Despite the universal properties of such (N ,z ) ensembles, our analysis further reveals that a classical mean-field approach fails to capture force distributions correctly. We demonstrate that network topology is a crucial determinant of force distributions in elastic spring networks.
Study on the evolutionary optimisation of the topology of network control systems
Zhou, Zude; Chen, Benyuan; Wang, Hong; Fan, Zhun
2010-08-01
Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition of performance parameters, the normalised indices for the evaluation of the topology optimisation are proposed. The topology optimisation problem is formulated as a multi-objective optimisation problem and the evolutionary algorithm is applied to solve it. Special communication characteristics of the industrial control network are considered in the optimisation process. In respect to the evolutionary algorithm design, an improved arena algorithm is proposed for the construction of the non-dominated set of the population. In addition, for the evaluation of individuals, the integrated use of the dominative relation method and the objective function combination method, for reducing the computational cost of the algorithm, are given. Simulation tests show that the performance of the proposed algorithm is preferable and superior compared to other algorithms. The final solution greatly improves the following indices: traffic localisation, traffic balance and utilisation rate balance of switches. In addition, a new performance index with its estimation process is proposed.
Poroseva, Svetlana
2012-02-01
The scale and complexity of modern networks, their integration, and the size of population and businesses they have impact on, make their massive damage catastrophic for the society and economy. Such damage is usually caused by adverse events and is not considered by traditional design practices. In the modern society, the likelihood of adverse events has substantially increased. Therefore, there is a need in evaluating the ability of a network to survive such damage. As the network topology is a key factor to consider, the goal of our research is to develop computational tools for quantifying its effect on the network survivability. ``Selfish'' algorithm will be presented that addresses exponential-time complexity associated with the problem of generation and analysis of all fault combinations possible in a given network. The reduction of computational complexity is achieved by mapping an initial network topology with multiple sources and sinks onto a set of simpler smaller topologies with multiple sources and a single sink. Application to the Texas power grid will be considered.
On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
Directory of Open Access Journals (Sweden)
Antonio-Javier Garcia-Sanchez
2016-05-01
Full Text Available Wireless Multimedia Sensor Networks (WMSNs are a special type of Wireless Sensor Network (WSN where large amounts of multimedia data are transmitted over networks composed of low power devices. Hierarchical routing protocols typically used in WSNs for multi-path communication tend to overload nodes located within radio communication range of the data collection unit or data sink. The battery life of these nodes is therefore reduced considerably, requiring frequent battery replacement work to extend the operational life of the WSN system. In a wireless sensor network with mesh topology, any node may act as a forwarder node, thereby enabling multiple routing paths toward any other node or collection unit. In addition, mesh topologies have proven advantages, such as data transmission reliability, network robustness against node failures, and potential reduction in energy consumption. This work studies the feasibility of implementing WMSNs in mesh topologies and their limitations by means of exhaustive computer simulation experiments. To this end, a module developed for the Synchronous Energy Saving (SES mode of the IEEE 802.15.5 mesh standard has been integrated with multimedia tools to thoroughly test video sequences encoded using H.264 in mesh networks.
On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies.
Garcia-Sanchez, Antonio-Javier; Losilla, Fernando; Rodenas-Herraiz, David; Cruz-Martinez, Felipe; Garcia-Sanchez, Felipe
2016-05-05
Wireless Multimedia Sensor Networks (WMSNs) are a special type of Wireless Sensor Network (WSN) where large amounts of multimedia data are transmitted over networks composed of low power devices. Hierarchical routing protocols typically used in WSNs for multi-path communication tend to overload nodes located within radio communication range of the data collection unit or data sink. The battery life of these nodes is therefore reduced considerably, requiring frequent battery replacement work to extend the operational life of the WSN system. In a wireless sensor network with mesh topology, any node may act as a forwarder node, thereby enabling multiple routing paths toward any other node or collection unit. In addition, mesh topologies have proven advantages, such as data transmission reliability, network robustness against node failures, and potential reduction in energy consumption. This work studies the feasibility of implementing WMSNs in mesh topologies and their limitations by means of exhaustive computer simulation experiments. To this end, a module developed for the Synchronous Energy Saving (SES) mode of the IEEE 802.15.5 mesh standard has been integrated with multimedia tools to thoroughly test video sequences encoded using H.264 in mesh networks.
International Nuclear Information System (INIS)
Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo
2015-01-01
Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively
Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo
2015-11-01
Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively
Regular Topologies for Gigabit Wide-Area Networks. Volume 1
Shacham, Nachum; Denny, Barbara A.; Lee, Diane S.; Khan, Irfan H.; Lee, Danny Y. C.; McKenney, Paul
1994-01-01
In general terms, this project aimed at the analysis and design of techniques for very high-speed networking. The formal objectives of the project were to: (1) Identify switch and network technologies for wide-area networks that interconnect a large number of users and can provide individual data paths at gigabit/s rates; (2) Quantitatively evaluate and compare existing and proposed architectures and protocols, identify their strength and growth potentials, and ascertain the compatibility of competing technologies; and (3) Propose new approaches to existing architectures and protocols, and identify opportunities for research to overcome deficiencies and enhance performance. The project was organized into two parts: 1. The design, analysis, and specification of techniques and protocols for very-high-speed network environments. In this part, SRI has focused on several key high-speed networking areas, including Forward Error Control (FEC) for high-speed networks in which data distortion is the result of packet loss, and the distribution of broadband, real-time traffic in multiple user sessions. 2. Congestion Avoidance Testbed Experiment (CATE). This part of the project was done within the framework of the DARTnet experimental T1 national network. The aim of the work was to advance the state of the art in benchmarking DARTnet's performance and traffic control by developing support tools for network experimentation, by designing benchmarks that allow various algorithms to be meaningfully compared, and by investigating new queueing techniques that better satisfy the needs of best-effort and reserved-resource traffic. This document is the final technical report describing the results obtained by SRI under this project. The report consists of three volumes: Volume 1 contains a technical description of the network techniques developed by SRI in the areas of FEC and multicast of real-time traffic. Volume 2 describes the work performed under CATE. Volume 3 contains the source
Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing
2016-01-01
Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371
Ecological network analysis of China's societal metabolism.
Zhang, Yan; Liu, Hong; Li, Yating; Yang, Zhifeng; Li, Shengsheng; Yang, Naijin
2012-01-01
Uncontrolled socioeconomic development has strong negative effects on the ecological environment, including pollution and the depletion and waste of natural resources. These serious consequences result from the high flows of materials and energy through a socioeconomic system produced by exchanges between the system and its surroundings, causing the disturbance of metabolic processes. In this paper, we developed an ecological network model for a societal system, and used China in 2006 as a case study to illustrate application of the model. We analyzed China's basic metabolic processes and used ecological network analysis to study the network relationships within the system. Basic components comprised the internal environment, five sectors (agriculture, exploitation, manufacturing, domestic, and recycling), and the external environment. We defined 21 pairs of ecological relationships in China's societal metabolic system (excluding self-mutualism within a component). Using utility and throughflow analysis, we found that exploitation, mutualism, and competition relationships accounted for 76.2, 14.3, and 9.5% of the total relationships, respectively. In our trophic level analysis, the components were divided into producers, consumers, and decomposers according to their positions in the system. Our analyses revealed ways to optimize the system's structure and adjust its functions, thereby promoting healthier socioeconomic development, and suggested ways to apply ecological network analysis in future socioeconomic research. Copyright © 2011 Elsevier Ltd. All rights reserved.
Topological structure of the space of phenotypes: the case of RNA neutral networks.
Directory of Open Access Journals (Sweden)
Jacobo Aguirre
Full Text Available The evolution and adaptation of molecular populations is constrained by the diversity accessible through mutational processes. RNA is a paradigmatic example of biopolymer where genotype (sequence and phenotype (approximated by the secondary structure fold are identified in a single molecule. The extreme redundancy of the genotype-phenotype map leads to large ensembles of RNA sequences that fold into the same secondary structure and can be connected through single-point mutations. These ensembles define neutral networks of phenotypes in sequence space. Here we analyze the topological properties of neutral networks formed by 12-nucleotides RNA sequences, obtained through the exhaustive folding of sequence space. A total of 4(12 sequences fragments into 645 subnetworks that correspond to 57 different secondary structures. The topological analysis reveals that each subnetwork is far from being random: it has a degree distribution with a well-defined average and a small dispersion, a high clustering coefficient, and an average shortest path between nodes close to its minimum possible value, i.e. the Hamming distance between sequences. RNA neutral networks are assortative due to the correlation in the composition of neighboring sequences, a feature that together with the symmetries inherent to the folding process explains the existence of communities. Several topological relationships can be analytically derived attending to structural restrictions and generic properties of the folding process. The average degree of these phenotypic networks grows logarithmically with their size, such that abundant phenotypes have the additional advantage of being more robust to mutations. This property prevents fragmentation of neutral networks and thus enhances the navigability of sequence space. In summary, RNA neutral networks show unique topological properties, unknown to other networks previously described.
Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup
2011-01-01
Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.
Probing the topological properties of complex networks modeling short written texts.
Directory of Open Access Journals (Sweden)
Diego R Amancio
Full Text Available In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well-many informative discoveries have been made this way-but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyses performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks.
Broad-scale small-world network topology induces optimal synchronization of flexible oscillators
International Nuclear Information System (INIS)
Markovič, Rene; Gosak, Marko; Marhl, Marko
2014-01-01
The discovery of small-world and scale-free properties of many man-made and natural complex networks has attracted increasing attention. Of particular interest is how the structural properties of a network facilitate and constrain its dynamical behavior. In this paper we study the synchronization of weakly coupled limit-cycle oscillators in dependence on the network topology as well as the dynamical features of individual oscillators. We show that flexible oscillators, characterized by near zero values of divergence, express maximal correlation in broad-scale small-world networks, whereas the non-flexible (rigid) oscillators are best correlated in more heterogeneous scale-free networks. We found that the synchronization behavior is governed by the interplay between the networks global efficiency and the mutual frequency adaptation. The latter differs for flexible and rigid oscillators. The results are discussed in terms of evolutionary advantages of broad-scale small-world networks in biological systems
Self-organizing Ising model of artificial financial markets with small-world network topology
Zhao, Haijie; Zhou, Jie; Zhang, Anghui; Su, Guifeng; Zhang, Yi
2013-01-01
We study a self-organizing Ising-like model of artificial financial markets with underlying small-world (SW) network topology. The asset price dynamics results from the collective decisions of interacting agents which are located on a small-world complex network (the nodes symbolize the agents of a financial market). The model incorporates the effects of imitation, the impact of external news and private information. We also investigate the influence of different network topologies, from regular lattice to random graph, on the asset price dynamics by adjusting the probability of the rewiring procedure. We find that a specific combination of model parameters reproduce main stylized facts of real-world financial markets.
International Nuclear Information System (INIS)
Wilson, Mark
2012-01-01
The effects of network topology on the static structural, mechanical and dynamic properties of MX 2 network-forming liquids (with tetrahedral short-range order) are discussed. The network topology is controlled via a single model parameter (the anion polarizability) which effectively constrains the inter-tetrahedral linkages in a physically transparent manner. Critically, it is found to control the balance between the stability of corner- and edge-sharing tetrahedra. A potential rigidity transformation is investigated. The vibrational density of states is investigated, using an instantaneous normal model analysis, as a function of both anion polarizability and temperature. A low frequency peak is seen to appear and is shown to be correlated with the fraction of cations which are linked through solely edge-sharing structural motifs. A modified effective mean atom coordination number is proposed which allows the appearance of the low frequency feature to be understood in terms of a mean field rigidity percolation threshold. (paper)
DEFF Research Database (Denmark)
Kirstein, Jonas Kjeld; Albrechtsen, Hans-Jørgen; Rygaard, Martin
2015-01-01
identification of potential contamination and affected consumers in contamination cases. Although still in development, the method shows potential for assisting utilities during planning of monitoring programs and as decision support tool during emergency contingency situations.......Topological clustering was explored as a tool for water supply utilities in preparation of monitoring and contamination contingency plans. A complex water distribution network model of Copenhagen, Denmark, was simplified by topological clustering into recognizable water movement patterns to: (1......) identify steady clusters for a part of the network where an actual contamination has occurred; (2) analyze this event by the use of mesh diagrams; and (3) analyze the use of mesh diagrams as a decision support tool for planning water quality monitoring. Initially, the network model was divided...
Genotype networks, innovation, and robustness in sulfur metabolism
2011-01-01
Background A metabolism is a complex network of chemical reactions. This network synthesizes multiple small precursor molecules of biomass from chemicals that occur in the environment. The metabolic network of any one organism is encoded by a metabolic genotype, defined as the set of enzyme-coding genes whose products catalyze the network's reactions. Each metabolic genotype has a metabolic phenotype. We define this metabolic phenotype as the spectrum of different sources of a chemical element that a metabolism can use to synthesize biomass. We here focus on the element sulfur. We study properties of the space of all possible metabolic genotypes in sulfur metabolism by analyzing random metabolic genotypes that are viable on different numbers of sulfur sources. Results We show that metabolic genotypes with the same phenotype form large connected genotype networks - networks of metabolic networks - that extend far through metabolic genotype space. How far they reach through this space depends linearly on the number of super-essential reactions. A super-essential reaction is an essential reaction that occurs in all networks viable in a given environment. Metabolic networks can differ in how robust their phenotype is to the removal of individual reactions. We find that this robustness depends on metabolic network size, and on other variables, such as the size of minimal metabolic networks whose reactions are all essential in a specific environment. We show that different neighborhoods of any genotype network harbor very different novel phenotypes, metabolic innovations that can sustain life on novel sulfur sources. We also analyze the ability of evolving populations of metabolic networks to explore novel metabolic phenotypes. This ability is facilitated by the existence of genotype networks, because different neighborhoods of these networks contain very different novel phenotypes. Conclusions We show that the space of metabolic genotypes involved in sulfur metabolism
Boaretto, B. R. R.; Budzinski, R. C.; Prado, T. L.; Kurths, J.; Lopes, S. R.
2018-05-01
It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider a network of 2000 thermally sensitive identical neurons, based on the model of Hodgkin-Huxley in a small-world topology, with the probability of adding non local connection equal to p = 0 . 001. Based on experimental protocols to suppress anomalous synchronization, as well as nonstationary behavior of the neural network dynamics, we make use of (i) external stimulus (pulsed current); (ii) biologic parameters changing (neuron membrane conductance changes); and (iii) body temperature changes. Quantification analysis to evaluate phase synchronization makes use of the Kuramoto's order parameter, while recurrence quantification analysis, particularly the determinism, computed over the easily accessible mean field of network, the local field potential (LFP), is used to evaluate nonstationary states. We show that the methods proposed can control the anomalous synchronization and nonstationarity occurring for weak coupling parameter without any effect on the individual neuron dynamics, neither in the expected asymptotic synchronized states occurring for large values of the coupling parameter.
Learning and innovative elements of strategy adoption rules expand cooperative network topologies.
Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter
2008-04-09
Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.
Learning and innovative elements of strategy adoption rules expand cooperative network topologies.
Directory of Open Access Journals (Sweden)
Shijun Wang
Full Text Available Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.
Limits and trade-offs of topological network robustness.
Priester, Christopher; Schmitt, Sebastian; Peixoto, Tiago P
2014-01-01
We investigate the trade-off between the robustness against random and targeted removal of nodes from a network. To this end we utilize the stochastic block model to study ensembles of infinitely large networks with arbitrary large-scale structures. We present results from numerical two-objective optimization simulations for networks with various fixed mean degree and number of blocks. The results provide strong evidence that three different blocks are sufficient to realize the best trade-off between the two measures of robustness, i.e. to obtain the complete front of Pareto-optimal networks. For all values of the mean degree, a characteristic three block structure emerges over large parts of the Pareto-optimal front. This structure can be often characterized as a core-periphery structure, composed of a group of core nodes with high degree connected among themselves and to a periphery of low-degree nodes, in addition to a third group of nodes which is disconnected from the periphery, and weakly connected to the core. Only at both extremes of the Pareto-optimal front, corresponding to maximal robustness against random and targeted node removal, a two-block core-periphery structure or a one-block fully random network are found, respectively.
Limits and trade-offs of topological network robustness.
Directory of Open Access Journals (Sweden)
Christopher Priester
Full Text Available We investigate the trade-off between the robustness against random and targeted removal of nodes from a network. To this end we utilize the stochastic block model to study ensembles of infinitely large networks with arbitrary large-scale structures. We present results from numerical two-objective optimization simulations for networks with various fixed mean degree and number of blocks. The results provide strong evidence that three different blocks are sufficient to realize the best trade-off between the two measures of robustness, i.e. to obtain the complete front of Pareto-optimal networks. For all values of the mean degree, a characteristic three block structure emerges over large parts of the Pareto-optimal front. This structure can be often characterized as a core-periphery structure, composed of a group of core nodes with high degree connected among themselves and to a periphery of low-degree nodes, in addition to a third group of nodes which is disconnected from the periphery, and weakly connected to the core. Only at both extremes of the Pareto-optimal front, corresponding to maximal robustness against random and targeted node removal, a two-block core-periphery structure or a one-block fully random network are found, respectively.
Space station common module network topology and hardware development
Anderson, P.; Braunagel, L.; Chwirka, S.; Fishman, M.; Freeman, K.; Eason, D.; Landis, D.; Lech, L.; Martin, J.; Mccorkle, J.
1990-01-01
Conceptual space station common module power management and distribution (SSM/PMAD) network layouts and detailed network evaluations were developed. Individual pieces of hardware to be developed for the SSM/PMAD test bed were identified. A technology assessment was developed to identify pieces of equipment requiring development effort. Equipment lists were developed from the previously selected network schematics. Additionally, functional requirements for the network equipment as well as other requirements which affected the suitability of specific items for use on the Space Station Program were identified. Assembly requirements were derived based on the SSM/PMAD developed requirements and on the selected SSM/PMAD network concepts. Basic requirements and simplified design block diagrams are included. DC remote power controllers were successfully integrated into the DC Marshall Space Flight Center breadboard. Two DC remote power controller (RPC) boards experienced mechanical failure of UES 706 stud-mounted diodes during mechanical installation of the boards into the system. These broken diodes caused input to output shorting of the RPC's. The UES 706 diodes were replaced on these RPC's which eliminated the problem. The DC RPC's as existing in the present breadboard configuration do not provide ground fault protection because the RPC was designed to only switch the hot side current. If ground fault protection were to be implemented, it would be necessary to design the system so the RPC switched both the hot and the return sides of power.
Effect of Network Structure/Topology on Mechanical Properties of Crosslinked Polymers
Sharifi, Majid
The interest in epoxy thermosetting polymers is widespread (e.g. Boeing 787 Dreamliner, windmill blades, automobiles, coatings, adhesives, etc.), and a demand still exists for improving toughness of these materials without degrading advantageous properties such as strength, modulus, and Tg. This study introduces novel approaches for improving the intrinsic mechanical characteristics of these polymers. The designed synthetic techniques focus on developing polymer materials with the same overall compositions but varying in network topologies, with distinct topological features in the size range of 5-50 nm, measured by SAXS and SEM. It was found that without altering chemical structure, the network topology of a dense thermoset can be engineered such that, under mechanical deformation, nano-cavities open and dissipate energy before rupturing covalent bonds, producing a tougher material without sacrificing strength, modulus, and even glass transition temperature. Modified structures also revealed higher resistance to fracture than the corresponding control structures. The major fracture mechanism responsible for the increased energy dissipation was found to be nano-cavitation. SEM images from the fracture surfaces showed clear cavities on the modified samples whereas none were seen on the fracture surface of the control samples. Overall, it was demonstrated that network topology can be used to tailor thermal and mechanical properties of thermosetting polymers. The experimental methodologies in this dissertation can directly and economically be applied to design polymeric materials with improved properties for desired applications. Although topology-based toughening was investigated on epoxy-amine polymers, the concept can be extended to most thermoset chemistries and perhaps to other brittle network forming materials.
Podder, Avijit; Jatana, Nidhi; Latha, N
2014-09-21
Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network. Copyright © 2014 Elsevier Ltd. All rights reserved.
2017-01-01
Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result. PMID:28133490
Directory of Open Access Journals (Sweden)
Yue Fan
2017-01-01
Full Text Available Gene regulatory networks (GRNs play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.
A Topological Description of Hubs in Amino Acid Interaction Networks
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Omar Gaci
2010-01-01
Full Text Available We represent proteins by amino acid interaction networks. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. Once we have compared this type of graphs to the general model of scale-free networks, we analyze the existence of nodes which highly interact, the hubs. We describe these nodes taking into account their position in the primary structure to study their apparition frequency in the folded proteins. Finally, we observe that their interaction level is a consequence of the general rules which govern the folding process.
Directory of Open Access Journals (Sweden)
Gosset Guillermo
2007-06-01
Full Text Available Abstract Background Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses. Results Transcriptome data from isogenic wild type and crp- strains grown in Luria-Bertani medium (LB or LB + 4 g/L glucose (LB+G were analyzed to identify differentially transcribed genes. We detected 180 and 200 genes displaying increased and reduced relative transcript levels in the presence of glucose, respectively. The observed expression pattern in LB was consistent with a gluconeogenic metabolic state including active transport and interconversion of small molecules and macromolecules, induction of protease-encoding genes and a partial heat shock response. In LB+G, catabolic repression was detected for transport and metabolic interconversion activities. We also detected an increased capacity for de novo synthesis of nucleotides, amino acids and proteins. Cluster analysis of a subset of genes revealed that CRP mediates catabolite repression for most of the genes displaying reduced transcript levels in LB+G, whereas Fis participates in the upregulation of genes under this condition. An analysis of the regulatory network, in terms of topological functional units, revealed 8 interconnected modules which again exposed the importance of Fis and CRP as directly responsible for the coordinated response of the cell. This effect was also seen with other not extensively connected transcription factors such as FruR and PdhR, which showed a consistent response considering media composition. Conclusion This work allowed the identification of eight interconnected regulatory network modules that includes CRP, Fis and other transcriptional factors that respond directly or indirectly to the
Alam, Mohammad Tauqeer; Medema, Marnix H.; Takano, Eriko; Breitling, Rainer; Gojobori, Takashi
2011-01-01
Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of
Alam, M.T.; Medema, M.H.; Takano, E.; Breitling, R.
2011-01-01
Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of
Energy efficient topology for Wireless Mesh Networks | Negash ...
African Journals Online (AJOL)
We analyze the power control problem using coalition formation game theory employing utilities based on the coverage areas of the access points by associating a cost function with the utility as the payoff of the coalition members. Our work focuses on the access layer of a wireless mesh local area network. We show that by ...
Correlation and network topologies in global and local stock indices
DEFF Research Database (Denmark)
Nobi, A.; Lee, S.; Kim, D. H.
2014-01-01
We examined how the correlation and network structure of the global indices and local Korean indices have changed during years 2000-2012. The average correlations of the global indices increased with time, while the local indices showed a decreasing trend except for drastic changes during...
Fault-Tolerant Topology Selection for TTEthernet Networks
DEFF Research Database (Denmark)
Gavrilut, Voica Maria; Tamas-Selicean, Domitian; Pop, Paul
2015-01-01
Many safety-critical real-time applications are implemented using distributed architectures, composed of heterogeneous processing elements (PEs) interconnected in a network. In this paper, we are interested in the TTEthernet protocol, which is a deterministic, synchronized and congestion-free net...
Directory of Open Access Journals (Sweden)
Nana Jin
2014-01-01
Full Text Available An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions, molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.
Topological Vulnerability Evaluation Model Based on Fractal Dimension of Complex Networks
Gou, Li; Wei, Bo; Sadiq, Rehan; Sadiq, Yong; Deng, Yong
2016-01-01
With an increasing emphasis on network security, much more attentions have been attracted to the vulnerability of complex networks. In this paper, the fractal dimension, which can reflect space-filling capacity of networks, is redefined as the origin moment of the edge betweenness to obtain a more reasonable evaluation of vulnerability. The proposed model combining multiple evaluation indexes not only overcomes the shortage of average edge betweenness’s failing to evaluate vulnerability of some special networks, but also characterizes the topological structure and highlights the space-filling capacity of networks. The applications to six US airline networks illustrate the practicality and effectiveness of our proposed method, and the comparisons with three other commonly used methods further validate the superiority of our proposed method. PMID:26751371
AlignNemo: a local network alignment method to integrate homology and topology.
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Giovanni Ciriello
Full Text Available Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.
Fraschini, Matteo; Demuru, Matteo; Hillebrand, Arjan; Cuccu, Lorenza; Porcu, Silvia; di Stefano, Francesca; Puligheddu, Monica; Floris, Gianluca; Borghero, Giuseppe; Marrosu, Francesco
2016-12-01
Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.
Directory of Open Access Journals (Sweden)
Yun Yu
Full Text Available Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa.
Alvarez-Silva, María Camila; Álvarez-Yela, Astrid Catalina; Gómez-Cano, Fabio; Zambrano, María Mercedes; Husserl, Johana; Danies, Giovanna; Restrepo, Silvia; González-Barrios, Andrés Fernando
2017-01-01
Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.
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María Camila Alvarez-Silva
Full Text Available Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.
Evidence for fish dispersal from spatial analysis of stream network topology
Hitt, N.P.; Angermeier, P.L.
2008-01-01
Developing spatially explicit conservation strategies for stream fishes requires an understanding of the spatial structure of dispersal within stream networks. We explored spatial patterns of stream fish dispersal by evaluating how the size and proximity of connected streams (i.e., stream network topology) explained variation in fish assemblage structure and how this relationship varied with local stream size. We used data from the US Environmental Protection Agency's Environmental Monitoring and Assessment Program in wadeable streams of the Mid-Atlantic Highlands region (n = 308 sites). We quantified stream network topology with a continuous analysis based on the rate of downstream flow accumulation from sites and with a discrete analysis based on the presence of mainstem river confluences (i.e., basin area >250 km2) within 20 fluvial km (fkm) from sites. Continuous variation in stream network topology was related to local species richness within a distance of ???10 fkm, suggesting an influence of fish dispersal within this spatial grain. This effect was explained largely by catostomid species, cyprinid species, and riverine species, but was not explained by zoogeographic regions, ecoregions, sampling period, or spatial autocorrelation. Sites near mainstem river confluences supported greater species richness and abundance of catostomid, cyprinid, and ictalurid fishes than did sites >20 fkm from such confluences. Assemblages at sites on the smallest streams were not related to stream network topology, consistent with the hypothesis that local stream size regulates the influence of regional dispersal. These results demonstrate that the size and proximity of connected streams influence the spatial distribution of fish and suggest that these influences can be incorporated into the designs of stream bioassessments and reserves to enhance management efficacy. ?? 2008 by The North American Benthological Society.
International Nuclear Information System (INIS)
Li, Jian; Dueñas-Osorio, Leonardo; Chen, Changkun; Shi, Congling
2016-01-01
As infrastructure systems evolve, their design, maintenance, and optimal performance require mature tools from system reliability theory, as well as principles to handle emerging system features, such as controllability. This paper conducts a comparative study of the connectivity reliability (CR) and topological controllability (TC) of infrastructure systems in terms of three aspects: topology, robustness, and node importance. Taking eight city-level power transmission networks and thousands of artificial networks as examples, this paper reveals that a dense and homogeneous network topology is better to satisfy CR and TC requirements, than more common sparse and heterogeneous networks when node attributes are generic. It is observed that the average degree's impact on CR is more significant than on TC, while degree heterogeneity is more significant on TC. When node attributes are accounted for, for generators the reliability-based node importance measure may underestimate some important nodes in terms of TC, and vice versa—an issue not observed for substation nodes. The findings in this paper suggest a potential new direction to enhance reliability-based design by integrating it with emerging controllability-based measures relevant in the future as infrastructure networks increase reliance on information systems. - Highlights: • Compares connectivity reliability (CR) and topological controllability (TC) metrics. • Develops a controllability index and a controllability-based node importance metric. • CR is more sensitive to degree while TC is more sensitive to degree heterogeneity. • CR-based importance measures match TC-based measures for substation nodes. • CR- and TC-based measures are complementary to identify important generator nodes.
The reconstruction and analysis of tissue specific human metabolic networks.
Hao, Tong; Ma, Hong-Wu; Zhao, Xue-Ming; Goryanin, Igor
2012-02-01
Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .
Fault-Tolerant Topology Selection for TTEthernet Networks
DEFF Research Database (Denmark)
Gavrilut, Voica Maria; Tamas-Selicean, Domitian; Pop, Paul
2015-01-01
Many safety-critical real-time applications are implemented using distributed architectures, composed of heterogeneous processing elements (PEs) interconnected in a network. In this paper, we are interested in the TTEthernet protocol, which is a deterministic, synchronized and congestion-free net....... We propose a Simulated Annealing meta-heuristic to solve this optimization problem. The proposed approach has been evaluated using a synthetic benchmark and a space case study, based on the Orion Crew Exploration Vehicle....
A Fault Tolerant Self-Routing Computer Network Topology
1987-01-01
Delay Characteristics’, IEEE Transactions on Communications, 153 pp. 1400-1416, December 1975. 29. R. M. Metcalfe and D.R. Boggs, " Ethernet ...loop, the local switch, the metro or interoffice facility, the tandem switch and the intercity facility. In addition to these, signaling is the "glue...Evolution 3. Metro /Interoffice Evolution 4. Tandem Switching Evolution 5. Intercity Facilities Evolution 6. Signaling Network Evolution 7. Interworklng of
Consensus of Multiagent Networks with Intermittent Interaction and Directed Topology
Directory of Open Access Journals (Sweden)
Li Xiao
2014-01-01
Full Text Available Intermittent interaction control is introduced to solve the consensus problem for second-order multiagent networks due to the limited sensing abilities and environmental changes periodically. And, we get some sufficient conditions for the agents to reach consensus with linear protocol from the theoretical findings by using the Lyapunov control approach. Finally, the validity of the theoretical results is validated through the numerical example.
Topology Optimisation for Energy Management in Underwater Sensor Networks
2015-01-01
CFAR : constant false alarm rate FAR: false alarm rate GA: genetic algorithm PS: pattern search SNR: signal-to-noise ratio 1. Introduction Sensor...nodes in the network. 2.2 Target detection model A constant false alarm rate ( CFAR ) (Poor, 1994) model has been used for target detection, where (1...each sensing node in the surveillance region has a CFAR , and (2) each sensor has a sensing radius rd, within which the sensor detects a target with
Analysis of topological relationships and network properties in the interactions of human beings.
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Ye Yuan
Full Text Available In the animal world, various kinds of collective motions have been found and proven to be efficient ways of carrying out some activities such as searching for food and avoiding predators. Many scholars research the interactions of collective behaviors of human beings according to the rules of collective behaviors of animals. Based on the Lennard-Jones potential function and a self-organization process, our paper proposes a topological communication model to simulate the collective behaviors of human beings. In the results of simulations, we find various types of collective behavior and fission behavior and discover the threshold for the emergence of collective behavior, which is the range five to seven for the number of topology K. According to the analysis of network properties of the model, the in-degree of individuals is always equal to the number of topology. In the stable state, the out-degrees of individuals distribute around the value of the number of topology K, except that the out-degree of a single individual is approximately double the out-degrees of the other individuals. In addition, under different initial conditions, some features of different kinds of networks emerge from the model. We also find the leader and herd mentality effects in the characteristics of the behaviors of human beings in our model. Thus, this work could be used to discover how to promote the emergence of beneficial group behaviors and prevent the emergence of harmful behaviors.
Thangavelautham, Jekanthan; D'Eleuterio, Gabriele M T
2012-04-01
A key challenge in evolving control systems for robots using neural networks is training tractability. Evolving monolithic fixed topology neural networks is shown to be intractable with limited supervision in high dimensional search spaces. Common strategies to overcome this limitation are to provide more supervision by encouraging particular solution strategies, manually decomposing the task and segmenting the search space and network. These strategies require a supervisor with domain knowledge and may not be feasible for difficult tasks where novel concepts are required. The alternate strategy is to use self-organized task decomposition to solve difficult tasks with limited supervision. The artificial neural tissue (ANT) approach presented here uses self-organized task decomposition to solve tasks. ANT inspired by neurobiology combines standard neural networks with a novel wireless signaling scheme modeling chemical diffusion of neurotransmitters. These chemicals are used to dynamically activate and inhibit wired network of neurons using a coarse-coding framework. Using only a global fitness function that does not encourage a predefined solution, modular networks of neurons are shown to self-organize and perform task decomposition. This approach solves the sign-following task found to be intractable with conventional fixed and variable topology networks. In this paper, key attributes of the ANT architecture that perform self-organized task decomposition are shown. The architecture is robust and scalable to number of neurons, synaptic connections, and initialization parameters.
Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro
2016-02-10
For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent's rule are applied to show a subtle trade-off between topological and wiring complexity.
Online Expansion Technology for Dynamic Topology Changing ZigBee Wireless Sensor Networks
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Md. Emdadul Haque
2014-03-01
Full Text Available In ZigBee, the router capable devices have restriction to accept a number of devices as children devices. A router capable device can not allow any new device to join as a child device if it reaches to the maximum capacity of children or depth limit. According to ZigBee specification each device has a permanent 64-bit MAC address. If a device joins a ZigBee network, it receives a short 16-bit MAC address from the parent device. If a device can not join a network, it isolates from the network and becomes an orphan node even though address spaces are available in the network. The orphan problem becomes worse when the topology of the network changes dynamically. In this paper we propose an online expansion technology to connect the maximum number of devices specially for dynamic topology changing ZigBee wireless sensor networks. The proposed technology shares available address spaces of the router devices to reduce the number of orphan nodes in the network.
Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network
DEFF Research Database (Denmark)
Förster, Jochen; Famili, I.; Fu, P.
2003-01-01
and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....
Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale
2013-01-01
Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.
Frequency enhancement in coupled noisy excitable elements: effects of network topology
Chiang, Wei-Yin; Lai, Pik-Yin; Chan, Chi-Keung
2013-07-01
Coupled excitable elements in the presence of noise can exhibit oscillatory behavior with non-trivial frequency dependence as the coupling strength of the system increases. The phenomenon of frequency enhancement (FE) occurs in some coupling regime, in which the elements can oscillate with a frequency higher than their uncoupled frequencies. In this paper, details of the FE are investigated by simulations of the FitzHugh-Nagumo model with different network topologies. It is found that the characteristics of FE, such as the maximal enhancement coupling, enhancement level etc, are functions of the network topology and spatial dimensions. The effect of excitability and the spatio-temporal patterns during FE are investigated to provide an intuitive picture for the enhancement mechanism. Interestingly, some of these characteristics of FE can be described by scaling laws; suggesting the existence of universality in the FE phenomenon. The relevance of these results to biological rhythms are also discussed.
The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
Directory of Open Access Journals (Sweden)
Qiuming Liu
2017-03-01
Full Text Available In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difﬁcult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model is constructed. The spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with ﬁve phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network.
Nickray, Mohsen; Afzali-Kusha, Ali; Jäntti, Riku
2015-01-01
In this paper, we propose a topology control technique to reduce the energy consumption of wireless sensor networks (WSNs). The technique makes use of both power control and power management methods. The algorithm uses the power management technique to put as many idle nodes as possible into the sleep mode while invoking the power control method to adjust the transmission range of the active nodes. On the contrary to earlier works in which both of these methods were used separately, in this a...
Fan, Yue; Wang, Xiao; Peng, Qinke
2017-01-01
Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab p...
Directory of Open Access Journals (Sweden)
Yaou Liu
Full Text Available OBJECTIVE: To investigate the topological alterations of the whole-brain white-matter (WM structural networks in patients with neuromyelitis optica (NMO. METHODS: The present study involved 26 NMO patients and 26 age- and sex-matched healthy controls. WM structural connectivity in each participant was imaged with diffusion-weighted MRI and represented in terms of a connectivity matrix using deterministic tractography method. Graph theory-based analyses were then performed for the characterization of brain network properties. A multiple linear regression analysis was performed on each network metric between the NMO and control groups. RESULTS: The NMO patients exhibited abnormal small-world network properties, as indicated by increased normalized characteristic path length, increased normalized clustering and increased small-worldness. Furthermore, largely similar hub distributions of the WM structural networks were observed between NMO patients and healthy controls. However, regional efficiency in several brain areas of NMO patients was significantly reduced, which were mainly distributed in the default-mode, sensorimotor and visual systems. Furthermore, we have observed increased regional efficiency in a few brain regions such as the orbital parts of the superior and middle frontal and fusiform gyri. CONCLUSION: Although the NMO patients in this study had no discernible white matter T2 lesions in the brain, we hypothesize that the disrupted topological organization of WM networks provides additional evidence for subtle, widespread cerebral WM pathology in NMO.
Protection of Passive Optical Networks by Using Ring Topology and Tunable Splitters
Directory of Open Access Journals (Sweden)
Pavel Lafata
2013-01-01
Full Text Available This article proposes an innovative method for protecting of passive optical networks (PONs, especially the central optical unit – optical line termination (OLT. PON networks are typically used in modern high-speed access networks, but there are also several specific applications, such as in business, army or science sector, which require a complex protection and backup system against failures and malfunctions. A standard tree or star topologies, which are usually used for PON networks, are significantly vulnerable mainly against the malfunctions and failures of OLT unit or feeder optical cable. The method proposed in this paper is focused on forming PON network with ring topology using passive optical splitters. The main idea is based on the possibility of placing both OLT units (primary and secondary on the opposite sides of the ring, which can potentially increase the resistance of network. This method is described in the article and scenarios and calculations using symmetric or tunable asymmetric passive optical splitters are included as well.
Trust-Aware and Low Energy Consumption Security Topology Protocol of Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Zuo Chen
2015-01-01
Full Text Available Wireless sensor network (WSN is a kind of distributed and self-organizing networks, in which the sensor nodes have limited communication bandwidth, memory, and limited energy. The topology construction of this network is usually vulnerable when attacked by malicious nodes. Besides, excessive energy consumption is a problem that can not be ignored. Therefore, this paper proposes a secure topology protocol of WSN which is trust-aware and of low energy consumption, called TLES. The TLES considers the trust value as an important factor affecting the behavior of node. In detail, the TLES would take trust value, residual energy of the nodes, and node density into consideration when selecting cluster head nodes. Then, TLES constructs these cluster head nodes by choosing the next hop node according to distance to base station (BS, nodes’ degrees, and residual energy, so as to establish a safe, reliable, and energy saving network. Experimental results show that the algorithm can effectively isolate the malicious node in the network and reduce the consumption of energy of the whole network.
The influence of passenger flow on the topology characteristics of urban rail transit networks
Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong
2017-05-01
Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.
Hopping in the Crowd to Unveil Network Topology
Asllani, Malbor; Carletti, Timoteo; Di Patti, Francesca; Fanelli, Duccio; Piazza, Francesco
2018-04-01
We introduce a nonlinear operator to model diffusion on a complex undirected network under crowded conditions. We show that the asymptotic distribution of diffusing agents is a nonlinear function of the nodes' degree and saturates to a constant value for sufficiently large connectivities, at variance with standard diffusion in the absence of excluded-volume effects. Building on this observation, we define and solve an inverse problem, aimed at reconstructing the a priori unknown connectivity distribution. The method gathers all the necessary information by repeating a limited number of independent measurements of the asymptotic density at a single node, which can be chosen randomly. The technique is successfully tested against both synthetic and real data and is also shown to estimate with great accuracy the total number of nodes.
Regime-dependent topological properties of biofuels networks
Kristoufek, Ladislav; Janda, Karel; Zilberman, David
2013-02-01
In this paper, we analyze the relationships between biodiesel, ethanol and related fuels and agricultural commodities with a use of minimal spanning trees and hierarchical trees. To distinguish between short term and long term effects, we construct the trees for different frequencies (weekly, monthly and quarterly). At lower frequencies, the system splits into two well separated branches - a fuels part and a food part. Biodiesel tends to the fuels branch and ethanol to the food branch. We especially concentrate on the links and comovements between the commodities for different stages of the market based on the level of prices of important commodities or groups of commodities. Such approach uncovers some more interesting results and, in general, confirms the separation of network into fuels and food for majority of analyzed cases.
Phylogeny of metabolic networks: A spectral graph theoretical ...
Indian Academy of Sciences (India)
Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of ...
Directory of Open Access Journals (Sweden)
Danielle S Bassett
2010-04-01
Full Text Available Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.
A note on the consensus finding problem in communication networks with switching topologies
Haskovec, Jan
2014-05-07
In this note, we discuss the problem of consensus finding in communication networks of agents with dynamically switching topologies. In particular, we consider the case of directed networks with unbalanced matrices of communication rates. We formulate sufficient conditions for consensus finding in terms of strong connectivity of the underlying directed graphs and prove that, given these conditions, consensus is found asymptotically. Moreover, we show that this consensus is an emergent property of the system, being encoded in its dynamics and not just an invariant of its initial configuration. © 2014 © 2014 Taylor & Francis.
Topology-induced spatial Bose-Einstein condensation for bosons on star-shaped optical networks
International Nuclear Information System (INIS)
Brunelli, I; Giusiano, G; Mancini, F P; Sodano, P; Trombettoni, A
2004-01-01
New coherent states may be induced by pertinently engineering the topology of a network. As an example, we consider the properties of non-interacting bosons on a star network, which may be realized with a dilute atomic gas in a star-shaped deep optical lattice. The ground state is localized around the star centre and it is macroscopically occupied below the Bose-Einstein condensation temperature T c . We show that T c depends only on the number of the star arms and on the Josephson energy of the bosonic Josephson junctions and that the non-condensate fraction is simply given by the reduced temperature T/T c
The topology of a causal network for the Chinese financial system
Gao, Bo; Ren, Ruo-en
2013-07-01
The paper builds a causal network for the Chinese financial system based on the Granger causality of company risks, studies its different topologies in crisis and bull period, and applies the centrality to explain individual risk and prevent systemic risk. The results show that this causal network possesses both small-world phenomenon and scale-free property, and has a little different average distance, clustering coefficient, and degree distribution in different periods, and financial institutions with high centrality not only have large individual risk, but also are important for systemic risk immunization.
Consumers' online social network topologies and health behaviours.
Lau, Annie Y S; Dunn, Adam; Mortimer, Nathan; Proudfoot, Judith; Andrews, Annie; Liaw, Siaw-Teng; Crimmins, Jacinta; Arguel, Amaël; Coiera, Enrico
2013-01-01
Personally controlled health management systems (PCHMS) often consist of multiple design features. Yet, they currently lack empirical evidence on how consumers use and engage with a PCHMS. An online prospective study was designed to investigate how 709 consumers used a web-based PCHMS to manage their physical and emotional wellbeing over five months. The web-based PCHMS, Healthy.me, was developed at UNSW and incorporates an untethered personal health record, consumer care pathways, forums, polls, diaries, and messaging links with healthcare professionals. The two PCHMS features that consumers used most frequently, found most useful, and engaging were the social features, i.e. forum and poll. Compared to participants who did not use any PCHMS social feature, those who used either the poll or the forum were 12.3% more likely to visit a healthcare professional (P=0.001) during the study. Social network analysis of forums revealed a spectrum of social interaction patterns - from question-and-answer structures to community discussions. This study provides a basis for understanding how a PCHMS can be used as a socially-driven intervention to influence consumers' health behaviours.
Evaluating the Limits of Network Topology Inference Via Virtualized Network Emulation
2015-06-01
Internetwork Operating System IP Internet Protocol IPv4 Internet Protocol Version 4 IPv6 Internet Protocol Version 6 ISP Internet Service Provider IXP...infer topology. Multicast-enabled Internet Protocol Version 4 ( IPv4 ) routers, when queried by mrinfo, would return results about neighboring multicast...CAIDA) and Réseaux IP Européens (RIPE) continuously probe and collect inferred snapshots of the global Internet topology for both IPv4 and Internet
Directory of Open Access Journals (Sweden)
Xiaogang Qi
2015-01-01
Full Text Available Wireless sensor network (WSN is a classical self-organizing communication network, and its topology evolution currently becomes one of the attractive issues in this research field. Accordingly, the problem is divided into two subproblems: one is to design a new preferential attachment method and the other is to analyze the dynamics of the network topology evolution. To solve the first subproblem, a revised PageRank algorithm, called Con-rank, is proposed to evaluate the node importance upon the existing node contraction, and then a novel preferential attachment is designed based on the node importance calculated by the proposed Con-rank algorithm. To solve the second one, we firstly analyze the network topology evolution dynamics in a theoretical way and then simulate the evolution process. Theoretical analysis proves that the network topology evolution of our model agrees with power-law distribution, and simulation results are well consistent with our conclusions obtained from the theoretical analysis and simultaneously show that our topology evolution model is superior to the classic BA model in the average path length and the clustering coefficient, and the network topology is more robust and can tolerate the random attacks.
Exploitation of complex network topology for link prediction in biological interactomes
Alanis Lobato, Gregorio
2014-06-01
The network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.
Motif formation and industry specific topologies in the Japanese business firm network
Maluck, Julian; Donner, Reik V.; Takayasu, Hideki; Takayasu, Misako
2017-05-01
Motifs and roles are basic quantities for the characterization of interactions among 3-node subsets in complex networks. In this work, we investigate how the distribution of 3-node motifs can be influenced by modifying the rules of an evolving network model while keeping the statistics of simpler network characteristics, such as the link density and the degree distribution, invariant. We exemplify this problem for the special case of the Japanese Business Firm Network, where a well-studied and relatively simple yet realistic evolving network model is available, and compare the resulting motif distribution in the real-world and simulated networks. To better approximate the motif distribution of the real-world network in the model, we introduce both subgraph dependent and global additional rules. We find that a specific rule that allows only for the merging process between nodes with similar link directionality patterns reduces the observed excess of densely connected motifs with bidirectional links. Our study improves the mechanistic understanding of motif formation in evolving network models to better describe the characteristic features of real-world networks with a scale-free topology.
Designing Structure-Dependent MPC-Based AGC Schemes Considering Network Topology
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Young-Sik Jang
2015-04-01
Full Text Available This paper presents the important features of structure-dependent model predictive control (MPC-based approaches for automatic generation control (AGC considering network topology. Since power systems have various generators under different topologies, it is necessary to reflect the characteristics of generators in power networks and the control system structures in order to improve the dynamic performance of AGC. Specifically, considering control system structures is very important because not only can the topological problems be reduced, but also a computing system for AGC in a bulk-power system can be realized. Based on these considerations, we propose new schemes in the proposed controller for minimizing inadvertent line flows and computational burden, which strengthen the advantages of MPC-based approach for AGC. Analysis and simulation results in the IEEE 39-bus model system show different dynamic behaviors among structure-dependent control schemes and possible improvements in computational burden via the proposed control scheme while system operators in each balancing area consider physical load reference ramp constraints among generators.
A method of examining the structure and topological properties of public-transport networks
Dimitrov, Stavri Dimitri; Ceder, Avishai (Avi)
2016-06-01
This work presents a new method of examining the structure of public-transport networks (PTNs) and analyzes their topological properties through a combination of computer programming, statistical data and large-network analyses. In order to automate the extraction, processing and exporting of data, a software program was developed allowing to extract the needed data from General Transit Feed Specification, thus overcoming difficulties occurring in accessing and collecting data. The proposed method was applied to a real-life PTN in Auckland, New Zealand, with the purpose of examining whether it showed characteristics of scale-free networks and exhibited features of ;small-world; networks. As a result, new regression equations were derived analytically describing observed, strong, non-linear relationships among the probabilities of randomly chosen stops in the PTN to be serviced by a given number of routes. The established dependence is best fitted by an exponential rather than a power-law function, showing that the PTN examined is neither random nor scale-free, but a mixture of the two. This finding explains the presence of hubs that are not typical of exponential networks and simultaneously not highly connected to the other nodes as is the case with scale-free networks. On the other hand, the observed values of the topological properties of the network show that although it is highly clustered, owing to its representation as a directed graph, it differs slightly from ;small-world; networks, which are characterized by strong clustering and a short average path length.
Critical conducting networks in disordered solids: ac universality from topological arguments
DEFF Research Database (Denmark)
Milovanov, A.V.; Juul Rasmussen, Jens
2001-01-01
This paper advocates an unconventional description of charge transport processes in disordered solids, which brings together the ideas of fractal geometry, percolation theory, and topology of manifolds. We demonstrate that the basic features of ac conductivity in disordered materials as seen in v...... prediction of the present model is the "hyperuniversal" scaling sigma(omega)proportional to omega (1/3) for ac conduction in two dimensions.......This paper advocates an unconventional description of charge transport processes in disordered solids, which brings together the ideas of fractal geometry, percolation theory, and topology of manifolds. We demonstrate that the basic features of ac conductivity in disordered materials as seen...... in various experiments are reproduced with remarkable accuracy by the conduction properties of percolating fractal networks near the threshold of percolation. The universal character of ac conductivity in three embedding dimensions is discussed in connection with the available experimental data. An important...
PACS mini refresher course. Local area network topologies, media, and routing.
Stewart, B K
1992-05-01
Picture archiving and communication systems (PACS) use local area networks (LANs) for the transfer of images and image-related data. There are several aspects of LANs that are used to describe and categorize them: topology, the media used for transmission, and the medium access control (MAC) protocols. The four basic topologies are the bus, tree, ring, and star; the four primary types of media used for LANs include coaxial cable, twisted-pair wire, fiberoptic cables, and wireless. The wireless LANs that use infrared and radio frequencies are new but promising. Of the three MACs presently being used--Ethernet, fiber distributed data interface (FDDI), and UltraNet--Ethernet is the most common. LANs can be interconnected by means of the repeater, bridge, router, and gateway internetworking devices. The future of LAN development lies in increased standardization and increased connectivity in order to make teleradiology a reality.
Non-abelian anyons and topological quantum information processing in 1D wire networks
Alicea, Jason
2012-02-01
Topological quantum computation provides an elegant solution to decoherence, circumventing this infamous problem at the hardware level. The most basic requirement in this approach is the ability to stabilize and manipulate particles exhibiting non-Abelian exchange statistics -- Majorana fermions being the simplest example. Curiously, Majorana fermions have been predicted to arise both in 2D systems, where non-Abelian statistics is well established, and in 1D, where exchange statistics of any type is ill-defined. An important question then arises: do Majorana fermions in 1D hold the same technological promise as their 2D counterparts? In this talk I will answer this question in the affirmative, describing how one can indeed manipulate and harness the non-Abelian statistics of Majoranas in a remarkably simple fashion using networks formed by quantum wires or topological insulator edges.
Determination of keystone species in CSM food web: A topological analysis of network structure
Directory of Open Access Journals (Sweden)
LiQin Jiang
2015-03-01
Full Text Available The importance of a species is correlated with its topological properties in a food web. Studies of keystone species provide the valuable theory and evidence for conservation ecology, biodiversity, habitat management, as well as the dynamics and stability of the ecosystem. Comparing with biological experiments, network methods based on topological structure possess particular advantage in the identification of keystone species. In present study, we quantified the relative importance of species in Carpinteria Salt Marsh food web by analyzing five centrality indices. The results showed that there were large differences in rankings species in terms of different centrality indices. Moreover, the correlation analysis of those centralities was studied in order to enhance the identifying ability of keystone species. The results showed that the combination of degree centrality and closeness centrality could better identify keystone species, and the keystone species in the CSM food web were identified as, Stictodora hancocki, small cyathocotylid, Pygidiopsoides spindalis, Phocitremoides ovale and Parorchis acanthus.
A model for phosphate glass topology considering the modifying ion sub-network
DEFF Research Database (Denmark)
Hermansen, Christian; Mauro, J.C.; Yue, Yuanzheng
2014-01-01
In the present paper we establish a temperature dependent constraint model of alkali phosphate glasses considering the structural and topological role of the modifying ion sub-network constituted by alkali ions and their non-bonding oxygen coordination spheres. The model is consistent with availa......In the present paper we establish a temperature dependent constraint model of alkali phosphate glasses considering the structural and topological role of the modifying ion sub-network constituted by alkali ions and their non-bonding oxygen coordination spheres. The model is consistent...... with available structural data by NMR and molecular dynamics simulation and dynamic data such glass transition temperature (Tg) and liquid fragility (m). Alkali phosphate glasses are exemplary systems for developing constraint model since the modifying cation network plays an important role besides the primary...... of the modifying ion sub-network, as the alkali ions must share non-bonding oxygen to satisfy their coordination requirements at higher alkali oxide contents. We argue that the systematically decreasing the Tg values of alkali phosphate glasses from Li2O to Na2O to Cs2O could be caused by a weakening...
A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
Gui, Jinsong; Zhou, Kai; Xiong, Naixue
2016-01-01
Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731
Testing statistical self-similarity in the topology of river networks
Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.
2010-01-01
Recent work has demonstrated that the topological properties of real river networks deviate significantly from predictions of Shreve's random model. At the same time the property of mean self-similarity postulated by Tokunaga's model is well supported by data. Recently, a new class of network model called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to replicate important topological features observed in real river networks. We investigate if the hypothesis of statistical self-similarity in the RSN model is supported by data on a set of 30 basins located across the continental United States that encompass a wide range of hydroclimatic variability. We demonstrate that the generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. The parameters describing the distribution of interior and exterior generators are tested to be statistically different and the difference is shown to produce the well-known Hack's law. The inter-basin variability of RSN parameters is found to be statistically significant. We also test generator dependence on two climatic indices, mean annual precipitation and radiative index of dryness. Some indication of climatic influence on the generators is detected, but this influence is not statistically significant with the sample size available. Finally, two key applications of the RSN model to hydrology and geomorphology are briefly discussed.
Tsiotas, Dimitrios; Polyzos, Serafeim
2018-02-01
This article studies the topological consistency of spatial networks due to node aggregation, examining the changes captured between different network representations that result from nodes' grouping and they refer to the same socioeconomic system. The main purpose of this study is to evaluate what kind of topological information remains unalterable due to node aggregation and, further, to develop a framework for linking the data of an empirical network with data of its socioeconomic environment, when the latter are available for hierarchically higher levels of aggregation, in an effort to promote the interdisciplinary research in the field of complex network analysis. The research question is empirically tested on topological and socioeconomic data extracted from the Greek Maritime Network (GMN) that is modeled as a non-directed multilayer (bilayer) graph consisting of a port-layer, where nodes represent ports, and a prefecture-layer, where nodes represent coastal and insular prefectural groups of ports. The analysis highlights that the connectivity (degree) of the GMN is the most consistent aspect of this multilayer network, which preserves both the topological and the socioeconomic information through node aggregation. In terms of spatial analysis and regional science, such effects illustrate the effectiveness of the prefectural administrative division for the functionality of the Greek maritime transportation system. Overall, this approach proposes a methodological framework that can enjoy further applications about the grouping effects induced on the network topology, providing physical, technical, socioeconomic, strategic or political insights.
Proceedings of the International Symposium on Topological Aspects of Critical Systems and Networks
Yakubo, Kousuke; Amitsuka, Hiroshi; Ishikawa, Goo; Machino, Kazuo; Nakagaki, Toshiyuki; Tanda, Satoshi; Yamada, Hideto; Kichiji, Nozomi
2007-07-01
I. General properties of networks. Physics of network security / Y.-C. Lai, X. Wand and C. H. Lai. Multi-state interacting particle systems on scale-free networks / N. Masuda and N. Konno. Homotopy Reduction of Complex Networks 18 / Y. Hiraoka and T. Ichinomiya. Analysis of the Susceptible-Infected-Susceptible Model on Complex Network / T. Ichinomiya -- II. Complexity in social science. Innovation and Development in a Random Lattice / J. Lahtinen. Long-tailed distributions in biological systems: revisit to Lognormals / N. Kobayashi ... [et al.]. Two-class structure of income distribution in the USA:exponential bulk and power-law tail / V. M. Yakovenko and A. Christian Silva. Power Law distributions in two community currencies / N. Kichiji and M. Nishibe -- III. Patterns in biological objects. Stoichiometric network analysis of nonlinear phenomena in rection mechanism for TWC converters / M. Marek ... [et al.]. Collective movement and morphogenesis of epithelial cells / H. Haga and K. Kawabata. Indecisive behavior of amoeba crossing an environmental barrier / S. Takagi ... [et al.]. Effects of amount of food on path selection in the transport network of an amoeboid organism / T. Nakagaki ... [et al.]. Light scattering study in double network gels / M. Fukunaya ... [et al.].Blood flow velocity in the choroid in punctate inner choroidopathy and Vogt-Koyanagi-Harada disease; amd multifractal analysis of choroidal blood flow in age-related macular degeneration / K. Yoshida ... [et al.]. Topological analysis of placental arteries: correlation with neonatal growth / H. Yamada and K. Yakubo -- IV. Criticality in pure and applied physics. Droplets in Disordered Metallic Quantum Critical Systems / A. H. Castro Neto and B. A. Jones. Importance of static disorder and inhomogeneous cooperative dynamics in heavy-fermion metals / O. O. Bernal. Competition between spin glass and Antiferromagnetic phases in heavy fermion materials / S. Sullow. Emergent Phases via Fermi surface
The topological organization of white matter network in internet gaming disorder individuals.
Zhai, Jinquan; Luo, Lin; Qiu, Lijun; Kang, Yongqiang; Liu, Bo; Yu, Dahua; Lu, Xiaoqi; Yuan, Kai
2017-12-01
White matter (WM) integrity abnormalities had been reported in Internet gaming disorder (IGD). Diffusion tensor imaging (DTI) tractography allows identification of WM tracts, potentially providing information about the integrity and organization of relevant underlying WM fiber tracts' architectures, which has been used to investigate the connectivity of cortical and subcortical structures in several brain disorders. Unfortunately, relatively little is known about the thoroughly circuit-level characterization of topological property changes of WM network with IGD. Sixteen right-hand adolescents with IGD participated in our study, according to the diagnostic criteria of IGD in DSM-5. Meanwhile, 16 age and gender-matched healthy controls were also enrolled. DTI tractography was employed to generate brain WM networks in IGD individuals and healthy controls. The 90 cortical and subcortical regions derived from AAL template were chosen as the nodes. The network parameters (i.e., Network strength, clustering coefficient, shortest path length, global efficiency, local efficiency, regional efficiency) were calculated and then correlated with the Internet addiction test (IAT) scores in IGD. IGD group showed decreased global efficiency, local efficiency and increased shortest path length. Further analysis revealed the reduced nodal efficiency in frontal cortex, anterior cingulate cortex and pallidium in IGD. In addition, the global efficiency of WM network was correlated with the IAT scores in IGD (r = -0.5927; p = 0.0155). We reported the abnormal topological organization of WM network in IGD and the association with the severity of IGD, which may provide new insights into the neural mechanism of IGD from WM network level.
Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.
Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing
2018-03-12
Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.
Directory of Open Access Journals (Sweden)
Leach Sonia M
2008-06-01
Full Text Available Abstract Background Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins. Results Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the
Optimal Channel Width Adaptation, Logical Topology Design, and Routing in Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Li Li
2009-01-01
Full Text Available Radio frequency spectrum is a finite and scarce resource. How to efficiently use the spectrum resource is one of the fundamental issues for multi-radio multi-channel wireless mesh networks. However, past research efforts that attempt to exploit multiple channels always assume channels of fixed predetermined width, which prohibits the further effective use of the spectrum resource. In this paper, we address how to optimally adapt channel width to more efficiently utilize the spectrum in IEEE802.11-based multi-radio multi-channel mesh networks. We mathematically formulate the channel width adaptation, logical topology design, and routing as a joint mixed 0-1 integer linear optimization problem, and we also propose our heuristic assignment algorithm. Simulation results show that our method can significantly improve spectrum use efficiency and network performance.
Secure adaptive topology control for wireless ad-hoc sensor networks.
Hsueh, Ching-Tsung; Li, Yu-Wei; Wen, Chih-Yu; Ouyang, Yen-Chieh
2010-01-01
This paper presents a secure decentralized clustering algorithm for wireless ad-hoc sensor networks. The algorithm operates without a centralized controller, operates asynchronously, and does not require that the location of the sensors be known a priori. Based on the cluster-based topology, secure hierarchical communication protocols and dynamic quarantine strategies are introduced to defend against spam attacks, since this type of attacks can exhaust the energy of sensor nodes and will shorten the lifetime of a sensor network drastically. By adjusting the threshold of infected percentage of the cluster coverage, our scheme can dynamically coordinate the proportion of the quarantine region and adaptively achieve the cluster control and the neighborhood control of attacks. Simulation results show that the proposed approach is feasible and cost effective for wireless sensor networks.
Simeonidis, Evangelos; Chandrasekaran, Sriram; Price, Nathan D
2013-01-01
The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.
Effects of threshold on the topology of gene co-expression networks.
Couto, Cynthia Martins Villar; Comin, César Henrique; Costa, Luciano da Fontoura
2017-09-26
Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.
Effects of behavioral patterns and network topology structures on Parrondo’s paradox
Ye, Ye; Cheong, Kang Hao; Cen, Yu-Wan; Xie, Neng-Gang
2016-11-01
A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.
Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine
2016-08-01
We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.
GH32 family activity: a topological approach through protein contact networks.
Cimini, Sara; Di Paola, Luisa; Giuliani, Alessandro; Ridolfi, Alessandra; De Gara, Laura
2016-11-01
The application of Protein Contact Networks methodology allowed to highlight a novel response of border region between the two domains to substrate binding. Glycoside hydrolases (GH) are enzymes that mainly hydrolyze the glycosidic bond between two carbohydrates or a carbohydrate and a non-carbohydrate moiety. These enzymes are involved in many fundamental and diverse biological processes in plants. We have focused on the GH32 family, including enzymes very similar in both sequence and structure, each having however clear specificities of substrate preferences and kinetic properties. Structural and topological differences among proteins of the GH32 family have been here identified by means of an emerging approach (Protein Contact network, PCN) based on the formalization of 3D structures as contact networks among amino-acid residues. The PCN approach proved successful in both reconstructing the already known functional domains and in identifying the structural counterpart of the properties of GH32 enzymes, which remain uncertain, like their allosteric character. The main outcome of the study was the discovery of the activation upon binding of the border (cleft) region between the two domains. This reveals the allosteric nature of the enzymatic activity for all the analyzed forms in the GH32 family, a character yet to be highlighted in biochemical studies. Furthermore, we have been able to recognize a topological signature (graph energy) of the different affinity of the enzymes towards small and large substrates.
Enoki, Ryosuke; Kuroda, Shigeru; Ono, Daisuke; Hasan, Mazahir T; Ueda, Tetsuo; Honma, Sato; Honma, Ken-ichi
2012-12-26
The circadian pacemaker in the hypothalamic suprachiasmatic nucleus (SCN) is a hierarchical multioscillator system in which neuronal networks play crucial roles in expressing coherent rhythms in physiology and behavior. However, our understanding of the neuronal network is still incomplete. Intracellular calcium mediates the input signals, such as phase-resetting stimuli, to the core molecular loop involving clock genes for circadian rhythm generation and the output signals from the loop to various cellular functions, including changes in neurotransmitter release. Using a unique large-scale calcium imaging method with genetically encoded calcium sensors, we visualized intracellular calcium from the entire surface of SCN slice in culture including the regions where autonomous clock gene expression was undetectable. We found circadian calcium rhythms at a single-cell level in the SCN, which were topologically specific with a larger amplitude and more delayed phase in the ventral region than the dorsal. The robustness of the rhythm was reduced but persisted even after blocking the neuronal firing with tetrodotoxin (TTX). Notably, TTX dissociated the circadian calcium rhythms between the dorsal and ventral SCN. In contrast, a blocker of gap junctions, carbenoxolone, had only a minor effect on the calcium rhythms at both the single-cell and network levels. These results reveal the topological specificity of the circadian calcium rhythm in the SCN and the presence of coupled regional pacemakers in the dorsal and ventral regions. Neuronal firings are not necessary for the persistence of the calcium rhythms but indispensable for the hierarchical organization of rhythmicity in the SCN.
Networks of neuroblastoma cells on porous silicon substrates reveal a small world topology
Marinaro, Giovanni
2015-01-01
The human brain is a tightly interweaving network of neural cells where the complexity of the network is given by the large number of its constituents and its architecture. The topological structure of neurons in the brain translates into its increased computational capabilities, low energy consumption, and nondeterministic functions, which differentiate human behavior from artificial computational schemes. In this manuscript, we fabricated porous silicon chips with a small pore size ranging from 8 to 75 nm and large fractal dimensions up to Df ∼ 2.8. In culturing neuroblastoma N2A cells on the described substrates, we found that those cells adhere more firmly to and proliferate on the porous surfaces compared to the conventional nominally flat silicon substrates, which were used as controls. More importantly, we observed that N2A cells on the porous substrates create highly clustered, small world topology patterns. We conjecture that neurons with a similar architecture may elaborate information more efficiently than in random or regular grids. Moreover, we hypothesize that systems of neurons on nano-scale geometry evolve in time to form networks in which the propagation of information is maximized. This journal is
Tan, Liang Z.; Louie, Steven G.
2014-03-01
2D molecular networks can be fabricated from a wide variety of molecular building blocks, arranged in many different configurations. Interactions between neighboring molecular building blocks result in the formation of new 2D materials. Examples of 2D organic topological insulators, that contain molecular building blocks and heavy elements arranged in a hexagonal lattice, have been recently proposed by Feng Liu and coworkers (Nano Lett., 13, 2842 (2013)). In this work, we present a systematic study of the design space of 2D molecular network topological insulators, elucidating the role of structure, symmetry, and composition of the networks. We show that the magnitude and presence of spin-orbit gaps in the electronic band structure is strongly dependent on the symmetry properties and arrangement of the individual components of the molecular lattice. We present general rules to maximize the magnitude of spin-orbit gaps and perform ab-initio calculations on promising structures derived from these guidelines. This work was supported by National Science Foundation Grant No. DMR10-1006184, the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Computational resources have been provided by the NSF through XSEDE resources at NICS.
Topology-selective jamming of fully-connected, code-division random-access networks
Polydoros, Andreas; Cheng, Unjeng
1990-01-01
The purpose is to introduce certain models of topology selective stochastic jamming and examine its impact on a class of fully-connected, spread-spectrum, slotted ALOHA-type random access networks. The theory covers dedicated as well as half-duplex units. The dominant role of the spatial duty factor is established, and connections with the dual concept of time selective jamming are discussed. The optimal choices of coding rate and link access parameters (from the users' side) and the jamming spatial fraction are numerically established for DS and FH spreading.
Statistical properties of the stock and credit market: RMT and network topology
Lim, Kyuseong; Kim, Min Jae; Kim, Sehyun; Kim, Soo Yong
We analyzed the dependence structure of the credit and stock market using random matrix theory and network topology. The dynamics of both markets have been spotlighted throughout the subprime crisis. In this study, we compared these two markets in view of the market-wide effect from random matrix theory and eigenvalue analysis. We found that the largest eigenvalue of the credit market as a whole preceded that of the stock market in the beginning of the financial crisis and that of two markets tended to be synchronized after the crisis. The correlation between the companies of both markets became considerably stronger after the crisis as well.
Maxwell rigidity and topological constraints in amorphous phase-change networks
International Nuclear Information System (INIS)
Micoulaut, M.; Otjacques, C.; Raty, J.-Y.; Bichara, C.
2011-01-01
By analyzing first-principles molecular-dynamics simulations of different telluride amorphous networks, we develop a method for the enumeration of radial and angular topological constraints, and show that the phase diagram of the most popular system Ge-Sb-Te can be split into two compositional elastic phases: a tellurium rich flexible phase and a stressed rigid phase that contains most of the materials used in phase-change applications. This sound atomic scale insight should open new avenues for the understanding of phase-change materials and other complex amorphous materials from the viewpoint of rigidity.
Metabolic networks of Cucurbita maxima phloem.
Fiehn, Oliver
2003-03-01
Metabolomic analysis aims at a comprehensive characterization of biological samples. Yet, biologically meaningful interpretations are often limited by the poor spatial and temporal resolution of the acquired data sets. One way to remedy this is to limit the complexity of the cell types being studied. Cucurbita maxima Duch. vascular exudates provide an excellent material for metabolomics in this regard. Using automated mass spectral deconvolution, over 400 components have been detected in these exudates, but only 90 of them were tentatively identified. Many amino compounds were found in vascular exudates from leaf petioles at concentrations several orders of magnitude higher than in tissue disks from the same leaves, whereas hexoses and sucrose were found in far lower amounts. In order to find the expected impact of assimilation rates on sugar levels, total phloem composition of eight leaves from four plants was followed over 4.5 days. Surprisingly, no diurnal rhythm was found for any of the phloem metabolites that was statistically valid for all eight leaves. Instead, each leaf had its own distinct vascular exudate profile similar to leaves from the same plant, but clearly different from leaves harvested from plants at the same developmental stage. Thirty to forty per cent of all metabolite levels of individual leaves were different from the average of all metabolite profiles. Using metabolic co-regulation analysis, similarities and differences between the exudate profiles were more accurately characterized through network computation, specifically with respect to nitrogen metabolism.
Angelovici, Ruthie; Fait, Aaron; Zhu, Xiaohong; Szymanski, Jedrzej; Feldmesser, Ester; Fernie, Alisdair R; Galili, Gad
2009-12-01
In order to elucidate transcriptional and metabolic networks associated with lysine (Lys) metabolism, we utilized developing Arabidopsis (Arabidopsis thaliana) seeds as a system in which Lys synthesis could be stimulated developmentally without application of chemicals and coupled this to a T-DNA insertion knockout mutation impaired in Lys catabolism. This seed-specific metabolic perturbation stimulated Lys accumulation starting from the initiation of storage reserve accumulation. Our results revealed that the response of seed metabolism to the inducible alteration of Lys metabolism was relatively minor; however, that which was observable operated in a modular manner. They also demonstrated that Lys metabolism is strongly associated with the operation of the tricarboxylic acid cycle while largely disconnected from other metabolic networks. In contrast, the inducible alteration of Lys metabolism was strongly associated with gene networks, stimulating the expression of hundreds of genes controlling anabolic processes that are associated with plant performance and vigor while suppressing a small number of genes associated with plant stress interactions. The most pronounced effect of the developmentally inducible alteration of Lys metabolism was an induction of expression of a large set of genes encoding ribosomal proteins as well as genes encoding translation initiation and elongation factors, all of which are associated with protein synthesis. With respect to metabolic regulation, the inducible alteration of Lys metabolism was primarily associated with altered expression of genes belonging to networks of amino acids and sugar metabolism. The combined data are discussed within the context of network interactions both between and within metabolic and transcriptional control systems.
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Directory of Open Access Journals (Sweden)
Ivan Glasser
2018-01-01
Full Text Available Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio
2018-01-01
Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.
Globally altered structural brain network topology in grapheme-color synesthesia.
Hänggi, Jürgen; Wotruba, Diana; Jäncke, Lutz
2011-04-13
Synesthesia is a perceptual phenomenon in which stimuli in one particular modality elicit a sensation within the same or another sensory modality (e.g., specific graphemes evoke the perception of particular colors). Grapheme-color synesthesia (GCS) has been proposed to arise from abnormal local cross-activation between grapheme and color areas because of their hyperconnectivity. Recently published studies did not confirm such a hyperconnectivity, although morphometric alterations were found in occipitotemporal, parietal, and frontal regions of synesthetes. We used magnetic resonance imaging surface-based morphometry and graph-theoretical network analyses to investigate the topology of structural brain networks in 24 synesthetes and 24 nonsynesthetes. Connectivity matrices were derived from region-wise cortical thickness correlations of 2366 different cortical parcellations across the whole cortex and from 154 more common brain divisions as well. Compared with nonsynesthetes, synesthetes revealed a globally altered structural network topology as reflected by reduced small-worldness, increased clustering, increased degree, and decreased betweenness centrality. Connectivity of the fusiform gyrus (FuG) and intraparietal sulcus (IPS) was changed as well. Hierarchical modularity analysis revealed increased intramodular and intermodular connectivity of the IPS in GCS. However, connectivity differences in the FuG and IPS showed a low specificity because of global changes. We provide first evidence that GCS is rooted in a reduced small-world network organization that is driven by increased clustering suggesting global hyperconnectivity within the synesthetes' brain. Connectivity alterations were widespread and not restricted to the FuG and IPS. Therefore, synesthetic experience might be only one phenotypic manifestation of the globally altered network architecture in GCS.
Topological properties and community detection of venture capital network: Evidence from China
Jin, Yonghong; Zhang, Qi; Li, Sai-Ping
2016-01-01
Financial networks have been extensively studied as examples of real world complex networks. Based on the data from Chinese GEM and SME board, we establish a venture capital (VC) network to study the statistical properties, topological properties and community structure of the Chinese venture capital network. The result shows that there are no dominant venture capital firms in China which act as hubs in the VC network, and multi-company syndication is not popular in China, meaning that the relationships among venture capital companies are weak. The network is robust under either random or intentional attack, and possesses small world property. We also find from its community structure that, venture capital companies are more concentrated in developed districts but the links within the same district are scarce as compared to the links between different developed districts, indicating that venture capital companies are more willing to syndicate with companies in other developed districts. Furthermore, venture capital companies which invest in the same industry have closer relations within their communities than those which do not invest in the same industry.
From genomes to in silico cells via metabolic networks
DEFF Research Database (Denmark)
Borodina, Irina; Nielsen, Jens
2005-01-01
Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of...... approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components....
A probabilistic dynamic energy model for ad-hoc wireless sensors network with varying topology
Al-Husseini, Amal
In this dissertation we investigate the behavior of Wireless Sensor Networks (WSNs) from the degree distribution and evolution perspective. In specific, we focus on implementation of a scale-free degree distribution topology for energy efficient WSNs. WSNs is an emerging technology that finds its applications in different areas such as environment monitoring, agricultural crop monitoring, forest fire monitoring, and hazardous chemical monitoring in war zones. This technology allows us to collect data without human presence or intervention. Energy conservation/efficiency is one of the major issues in prolonging the active life WSNs. Recently, many energy aware and fault tolerant topology control algorithms have been presented, but there is dearth of research focused on energy conservation/efficiency of WSNs. Therefore, we study energy efficiency and fault-tolerance in WSNs from the degree distribution and evolution perspective. Self-organization observed in natural and biological systems has been directly linked to their degree distribution. It is widely known that scale-free distribution bestows robustness, fault-tolerance, and access efficiency to system. Fascinated by these properties, we propose two complex network theoretic self-organizing models for adaptive WSNs. In particular, we focus on adopting the Barabasi and Albert scale-free model to fit into the constraints and limitations of WSNs. We developed simulation models to conduct numerical experiments and network analysis. The main objective of studying these models is to find ways to reducing energy usage of each node and balancing the overall network energy disrupted by faulty communication among nodes. The first model constructs the wireless sensor network relative to the degree (connectivity) and remaining energy of every individual node. We observed that it results in a scale-free network structure which has good fault tolerance properties in face of random node failures. The second model considers
Directory of Open Access Journals (Sweden)
Ashok Panigrahy
2015-01-01
Full Text Available Patients with congenital heart disease (CHD are at risk for neurocognitive impairments. Little is known about the impact of CHD on the organization of large-scale brain networks. We applied graph analysis techniques to diffusion tensor imaging (DTI data obtained from 49 adolescents with dextro-transposition of the great arteries (d-TGA repaired with the arterial switch operation in early infancy and 29 healthy referent adolescents. We examined whether differences in neurocognitive functioning were related to white matter network topology. We developed mediation models revealing the respective contributions of peri-operative variables and network topology on cognitive outcome. Adolescents with d-TGA had reduced global efficiency at a trend level (p = 0.061, increased modularity (p = 0.012, and increased small-worldness (p = 0.026 as compared to controls. Moreover, these network properties mediated neurocognitive differences between the d-TGA and referent adolescents across every domain assessed. Finally, structural network topology mediated the neuroprotective effect of longer duration of core cooling during reparative neonatal cardiac surgery, as well as the detrimental effects of prolonged hospitalization. Taken together, worse neurocognitive function in adolescents with d-TGA is mediated by global differences in white matter network topology, suggesting that disruption of this configuration of large-scale networks drives neurocognitive dysfunction. These data provide new insights into the interplay between perioperative factors, brain organization, and cognition in patients with complex CHD.
Liu, Lanfang; Li, Hehui; Zhang, Manli; Wang, Zhengke; Wei, Na; Liu, Li; Meng, Xiangzhi; Ding, Guosheng
2016-07-01
Prior work has extensively studied neural deficits in children with reading impairment (RI) in their native language but has rarely examined those of RI children in their second language (L2). A recent study revealed that the function of the local brain regions was disrupted in children with RI in L2, but it is not clear whether the disruption also occurs at a large-scale brain network level. Using fMRI and graph theoretical analysis, we explored the topology of the whole-brain functional network during a phonological rhyming task and network reconfigurations across task and short resting phases in Chinese children with English reading impairment versus age-matched typically developing (TD) children. We found that, when completing the phonological task, the RI group exhibited higher local network efficiency and network modularity compared with the TD group. When switching between the phonological task and the short resting phase, the RI group showed difficulty with network reconfiguration, as reflected in fewer changes in the local efficiency and modularity properties and less rearrangement of the modular communities. These findings were reproducible after controlling for the effects of in-scanner accuracy, participant gender, and L1 reading performance. The results from the whole-brain network analyses were largely replicated in the task-activated network. These findings provide preliminary evidence supporting that RI in L2 is associated with not only abnormal functional network organization but also poor flexibility of the neural system in responding to changing cognitive demands. © 2016 John Wiley & Sons Ltd.
Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming
2017-10-01
Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.
Nickerson, Naomi H; Li, Ying; Benjamin, Simon C
2013-01-01
A scalable quantum computer could be built by networking together many simple processor cells, thus avoiding the need to create a single complex structure. The difficulty is that realistic quantum links are very error prone. A solution is for cells to repeatedly communicate with each other and so purify any imperfections; however prior studies suggest that the cells themselves must then have prohibitively low internal error rates. Here we describe a method by which even error-prone cells can perform purification: groups of cells generate shared resource states, which then enable stabilization of topologically encoded data. Given a realistically noisy network (≥10% error rate) we find that our protocol can succeed provided that intra-cell error rates for initialisation, state manipulation and measurement are below 0.82%. This level of fidelity is already achievable in several laboratory systems.
Robust node estimation and topology discovery for large-scale networks
Alouini, Mohamed-Slim
2017-02-23
Various examples are provided for node estimation and topology discovery for networks. In one example, a method includes receiving a packet having an identifier from a first node; adding the identifier to another transmission packet based on a comparison between the first identifier and existing identifiers associated with the other packet; adjusting a transmit probability based on the comparison; and transmitting the other packet based on a comparison between the transmit probability and a probability distribution. In another example, a system includes a network device that can adds an identifier received in a packet to a list including existing identifiers and adjust a transmit probability based on a comparison between the identifiers; and transmit another packet based on a comparison between the transmit probability and a probability distribution. In another example, a method includes determining a quantity of sensor devices based on a plurality of identifiers received in a packet.
Calculating degree-based topological indices of dominating David derived networks
Ahmad, Muhammad Saeed; Nazeer, Waqas; Kang, Shin Min; Imran, Muhammad; Gao, Wei
2017-12-01
An important area of applied mathematics is the Chemical reaction network theory. The behavior of real world problems can be modeled by using this theory. Due to applications in theoretical chemistry and biochemistry, it has attracted researchers since its foundation. It also attracts pure mathematicians because it involves interesting mathematical structures. In this report, we compute newly defined topological indices, namely, Arithmetic-Geometric index (AG1 index), SK index, SK1 index, and SK2 index of the dominating David derived networks [type="bibr" rid="j_phys-2017-0126_ref_001">1, type="bibr" rid="j_phys-2017-0126_ref_002">2, type="bibr" rid="j_phys-2017-0126_ref_003">3, type="bibr" rid="j_phys-2017-0126_ref_004">4, type="bibr" rid="j_phys-2017-0126_ref_005">5].
Huang, Yuanpeng Janet; Tejero, Roberto; Powers, Robert; Montelione, Gaetano T
2006-03-15
This article formulates the multidimensional nuclear Overhauser effect spectroscopy (NOESY) interpretation problem using graph theory and presents a novel, bottom-up, topology-constrained distance network analysis algorithm for NOESY cross peak interpretation using assigned resonances. AutoStructure is a software suite that implements this topology-constrained distance network analysis algorithm and iteratively generates structures using the three-dimensional (3D) protein structure calculation programs XPLOR/CNS or DYANA. The minimum input for AutoStructure includes the amino acid sequence, a list of resonance assignments, and lists of 2D, 3D, and/or 4D-NOESY cross peaks. AutoStructure can also analyze homodimeric proteins when X-filtered NOESY experiments are available. The quality of input data and final 3D structures is evaluated using recall, precision, and F-measure (RPF) scores, a statistical measure of goodness of fit with the input data. AutoStructure has been tested on three protein NMR data sets for which high-quality structures have previously been solved by an expert, and yields comparable high-quality distance constraint lists and 3D protein structures in hours. We also compare several protein structures determined using AutoStructure with corresponding homologous proteins determined with other independent methods. The program has been used in more than two dozen protein structure determinations, several of which have already been published. (c) 2005 Wiley-Liss, Inc.
Liu, Bo; Chen, Tianping
2008-11-01
In this brief, we provide some theoretical analysis of the consensus for networks of agents via stochastically switching topologies. We consider both discrete-time case and continuous-time case. The main contribution of this brief is that the underlying graph topology is more general in both cases than those appeared in previous papers. The weight matrix of the coupling graph is not assumed to be nonnegative or Metzler. That is, in the model discussed here, the off-diagonal entries of the weight matrix of the coupling graph may be negative. This means that sometimes, the coupling may not benefit, but may prevent the consensus of the coupled agents. In the continuous-time case, the switching time intervals also take a more general form of random variables than those appeared in previous works. We focus our study on such networks and give sufficient conditions that ensure almost sure consensus in both discrete-time case and continuous-time case. As applications, we give several corollaries under more specific assumptions, i.e., the switching can be some independent and identically distributed (i.i.d.) random variable series or a Markov chain. Numerical examples are also provided in both discrete-time and continuous-time cases to demonstrate the validity of our theoretical results.
Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks
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Roc Meseguer
2014-02-01
Full Text Available The adoption of mobile and ubiquitous solutions that involve participatory or opportunistic sensing increases every day. This situation has highlighted the relevance of optimizing the energy consumption of these solutions, because their operation depends on the devices’ battery lifetimes. This article presents a study that intends to understand how the prediction of topology control messages in human-centric wireless sensor networks can be used to help reduce the energy consumption of the participating devices. In order to do that, five research questions have been defined and a study based on simulations was conducted to answer these questions. The obtained results help identify suitable mobile computing scenarios where the prediction of topology control messages can be used to save energy of the network nodes. These results also allow estimating the percentage of energy saving that can be expected, according to the features of the work scenario and the participants behavior. Designers of mobile collaborative applications that involve participatory or opportunistic sensing, can take advantage of these findings to increase the autonomy of their solutions.
Network-based statistical comparison of citation topology of bibliographic databases
Šubelj, Lovro; Fiala, Dalibor; Bajec, Marko
2014-01-01
Modern bibliographic databases provide the basis for scientific research and its evaluation. While their content and structure differ substantially, there exist only informal notions on their reliability. Here we compare the topological consistency of citation networks extracted from six popular bibliographic databases including Web of Science, CiteSeer and arXiv.org. The networks are assessed through a rich set of local and global graph statistics. We first reveal statistically significant inconsistencies between some of the databases with respect to individual statistics. For example, the introduced field bow-tie decomposition of DBLP Computer Science Bibliography substantially differs from the rest due to the coverage of the database, while the citation information within arXiv.org is the most exhaustive. Finally, we compare the databases over multiple graph statistics using the critical difference diagram. The citation topology of DBLP Computer Science Bibliography is the least consistent with the rest, while, not surprisingly, Web of Science is significantly more reliable from the perspective of consistency. This work can serve either as a reference for scholars in bibliometrics and scientometrics or a scientific evaluation guideline for governments and research agencies. PMID:25263231
Glazebrook, Jane; Chen, Wenqiong; Estes, Bram; Chang, Hur-Song; Nawrath, Christiane; Métraux, Jean-Pierre; Zhu, Tong; Katagiri, Fumiaki
2003-04-01
The signal transduction network controlling plant responses to pathogens includes pathways requiring the signal molecules salicylic acid (SA), jasmonic acid (JA), and ethylene (ET). The network topology was explored using global expression phenotyping of wild-type and signaling-defective mutant plants, including eds3, eds4, eds5, eds8, pad1, pad2, pad4, NahG, npr1, sid2, ein2, and coi1. Hierarchical clustering was used to define groups of mutations with similar effects on gene expression and groups of similarly regulated genes. Mutations affecting SA signaling formed two groups: one comprised of eds4, eds5, sid2, and npr1-3 affecting only SA signaling; and the other comprised of pad2, eds3, npr1-1, pad4, and NahG affecting SA signaling as well as another unknown process. Major differences between the expression patterns in NahG and the SA biosynthetic mutant sid2 suggest that NahG has pleiotropic effects beyond elimination of SA. A third group of mutants comprised of eds8, pad1, ein2, and coi1 affected ethylene and jasmonate signaling. Expression patterns of some genes revealed mutual inhibition between SA- and JA-dependent signaling, while other genes required JA and ET signaling as well as the unknown signaling process for full expression. Global expression phenotype similarities among mutants suggested, and experiments confirmed, that EDS3 affects SA signaling while EDS8 and PAD1 affect JA signaling. This work allowed modeling of network topology, definition of co-regulated genes, and placement of previously uncharacterized regulatory genes in the network.
Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network
Mart?n-Jim?nez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; Gonz?lez, Janneth
2017-01-01
Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework t...
Leveraging Fog Computing for Scalable IoT Datacenter Using Spine-Leaf Network Topology
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K. C. Okafor
2017-01-01
Full Text Available With the Internet of Everything (IoE paradigm that gathers almost every object online, huge traffic workload, bandwidth, security, and latency issues remain a concern for IoT users in today’s world. Besides, the scalability requirements found in the current IoT data processing (in the cloud can hardly be used for applications such as assisted living systems, Big Data analytic solutions, and smart embedded applications. This paper proposes an extended cloud IoT model that optimizes bandwidth while allowing edge devices (Internet-connected objects/devices to smartly process data without relying on a cloud network. Its integration with a massively scaled spine-leaf (SL network topology is highlighted. This is contrasted with a legacy multitier layered architecture housing network services and routing policies. The perspective offered in this paper explains how low-latency and bandwidth intensive applications can transfer data to the cloud (and then back to the edge application without impacting QoS performance. Consequently, a spine-leaf Fog computing network (SL-FCN is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment. This approach is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications.
Comparisons of topological properties in autism for the brain network construction methods
Lee, Min-Hee; Kim, Dong Youn; Lee, Sang Hyeon; Kim, Jin Uk; Chung, Moo K.
2015-03-01
Structural brain networks can be constructed from the white matter fiber tractography of diffusion tensor imaging (DTI), and the structural characteristics of the brain can be analyzed from its networks. When brain networks are constructed by the parcellation method, their network structures change according to the parcellation scale selection and arbitrary thresholding. To overcome these issues, we modified the Ɛ -neighbor construction method proposed by Chung et al. (2011). The purpose of this study was to construct brain networks for 14 control subjects and 16 subjects with autism using both the parcellation and the Ɛ-neighbor construction method and to compare their topological properties between two methods. As the number of nodes increased, connectedness decreased in the parcellation method. However in the Ɛ-neighbor construction method, connectedness remained at a high level even with the rising number of nodes. In addition, statistical analysis for the parcellation method showed significant difference only in the path length. However, statistical analysis for the Ɛ-neighbor construction method showed significant difference with the path length, the degree and the density.
Wang, Qiang; Hong, Ming; Su, Zhongqing
2016-07-01
A sparse sensor network, based on the concept of semi-decentralized and standardized sensing, is developed, to actively excite and acquire cylindrical waves for damage identification and health monitoring of pipe structures. Differentiating itself from conventional ‘ring-style’ transducer arrays which attempt to steer longitudinal axisymmetric cylindrical waves via transducer synchronism, this sparse sensor network shows advantages in some aspects, including the use of fewer sensors, simpler manipulation, quicker configuration, less mutual dependence among sensors, and an improved signal-to-noise ratio. The sparse network is expanded topologically, aimed at eliminating the presence of ‘blind zones’ and the challenges associated with multi-path propagation of cylindrical waves. Theoretical analysis is implemented to comprehend propagation characteristics of waves guided by a cylindrical structure. A probability-based diagnostic imaging algorithm is introduced to visualize damage in pixelated images in an intuitive, prompt, and automatic manner. A self-contained health monitoring system is configured for experimental validation, via which quantitative identification of mono- and multi-damage in a steel cylinder is demonstrated. The results highlight an expanded sensing coverage of the sparse sensor network and its enhanced capacity of acquiring rich information, avoiding the cost of augmenting the number of sensors in a sensor network.
Graph methods for the investigation of metabolic networks in parasitology.
Cottret, Ludovic; Jourdan, Fabien
2010-08-01
Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.
Network topology and functional connectivity disturbances precede the onset of Huntington's disease.
Harrington, Deborah L; Rubinov, Mikail; Durgerian, Sally; Mourany, Lyla; Reece, Christine; Koenig, Katherine; Bullmore, Ed; Long, Jeffrey D; Paulsen, Jane S; Rao, Stephen M
2015-08-01
Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease
Becoming-Topologies of Education: Deformations, Networks and the Database Effect
Thompson, Greg; Cook, Ian
2015-01-01
This article uses topological approaches to suggest that education is becoming-topological. Analyses presented in a recent double-issue of "Theory, Culture & Society" are used to demonstrate the utility of topology for education. In particular, the article explains education's topological character through examining the global…
Metabolic pathways variability and sequence/networks comparisons
Tun, Kyaw; Dhar, Pawan K; Palumbo, Maria Concetta; Giuliani, Alessandro
2006-01-01
Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process. PMID:16420696
Metabolic pathways variability and sequence/networks comparisons
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Palumbo Maria
2006-01-01
Full Text Available Abstract Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint steps of the metabolic process.
Yao, Zhijun; Hu, Bin; Chen, Xuejiao; Xie, Yuanwei; Gutknecht, Jürg; Majoe, Dennis
2018-02-01
This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.
Research on the Topological Properties of Air Quality Index Based on a Complex Network
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Yongli Zhang
2018-04-01
Full Text Available To analyze the dynamic characteristics of air quality for enforcing effective measures to prevent and evade air pollution harm, air quality index (AQI time series data was selected and transformed into a symbol sequence consisting of characters (H, M, L through the coarse graining process; then each 6-symbols series was treated as one vertex by time sequence to construct the AQI directed-weighted network; finally the centrality, clusterability, and ranking of the AQI network were analyzed. The results indicated that vertex strength and cumulative strength distribution, vertex strength and strength rank presented power law distributions, and the AQI network is a scale-free network. Only 17 vertices possessed a higher weighted clustering coefficient; meanwhile weighted clustering coefficient and vertex strength didn’t show a strong correlation. The AQI network did not have an obvious central tendency towards intermediaries in general, but 20.55% of vertices accounted for nearly 1/2 of the intermediaries, and the varieties still existed. The mean distance of 68.4932% of vertices was 6.120–9.973, the AQI network did not have obvious small-world phenomena, the conversion of AQI patterns presented the characteristics of periodicity and regularity, and 20.2055% of vertices had high proximity prestige. The vertices fell into six islands, the AQI pattern indicating heavy or serious air pollution lasting six days always lingered for a long time. The number of triads 2-012 was the largest, and the AQI network followed the transitivity model. The study has instructional significance in understanding time change regulation of air quality in Beijing, opening a new way for time series prediction research. Additionally, the factors causing the change of topological properties should be analyzed in the future research.
A fault-tolerant small world topology control model in ad hoc networks for search and rescue
Tan, Mian; Fang, Ling; Wu, Yue; Zhang, Bo; Chang, Bowen; Holme, Petter; Zhao, Jing
2018-02-01
Due to their self-organized, multi-hop and distributed characteristics, ad hoc networks are useful in search and rescue. Topology control models need to be designed for energy-efficient, robust and fast communication in ad hoc networks. This paper proposes a topology control model which specializes for search and rescue-Compensation Small World-Repeated Game (CSWRG)-which integrates mobility models, constructing small world networks and a game-theoretic approach to the allocation of resources. Simulation results show that our mobility models can enhance the communication performance of the constructed small-world networks. Our strategy, based on repeated game, can suppress selfish behavior and compensate agents that encounter selfish or faulty neighbors. This model could be useful for the design of ad hoc communication networks.
Optical image encryption topology.
Yong-Liang, Xiao; Xin, Zhou; Qiong-Hua, Wang; Sheng, Yuan; Yao-Yao, Chen
2009-10-15
Optical image encryption topology is proposed based on the principle of random-phase encoding. Various encryption topological units, involving peer-to-peer, ring, star, and tree topologies, can be realized by an optical 6f system. These topological units can be interconnected to constitute an optical image encryption network. The encryption and decryption can be performed in both digital and optical methods.
Harston, Craig; Schumacher, Chris
1992-01-01
Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent.
Detecting and diagnosing SSME faults using an autoassociative neural network topology
Ali, M.; Dietz, W. E.; Kiech, E. L.
1989-01-01
An effort is underway at the University of Tennessee Space Institute to develop diagnostic expert system methodologies based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. Neural networks are being investigated as a means of storing and retrieving fault scenarios. Neural networks offer several powerful features in fault diagnosis, including (1) general pattern matching capabilities, (2) resistance to noisy input data, (3) the ability to be trained by example, and (4) the potential for implementation on parallel computer architectures. This paper presents (1) an autoassociative neural network topology, i.e. the network input and output is identical when properly trained, and hence learning is unsupervised; (2) the training regimen used; and (3) the response of the system to inputs representing both previously observed and unkown fault scenarios. The effects of noise on the integrity of the diagnosis are also evaluated.
The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.
Weng, Jian; Xie, Ye; Wang, Chunjie; Chen, Feiyan
2017-08-18
Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.
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Elena Zotenko
2008-08-01
Full Text Available The centrality-lethality rule, which notes that high-degree nodes in a protein interaction network tend to correspond to proteins that are essential, suggests that the topological prominence of a protein in a protein interaction network may be a good predictor of its biological importance. Even though the correlation between degree and essentiality was confirmed by many independent studies, the reason for this correlation remains illusive. Several hypotheses about putative connections between essentiality of hubs and the topology of protein-protein interaction networks have been proposed, but as we demonstrate, these explanations are not supported by the properties of protein interaction networks. To identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality, we performed a rigorous analysis of six variants of the genomewide protein interaction network for Saccharomyces cerevisiae obtained using different techniques. We demonstrated that the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. Moreover, we rejected two previously proposed explanations for the centrality-lethality rule, one relating the essentiality of hubs to their role in the overall network connectivity and another relying on the recently published essential protein interactions model.
International Nuclear Information System (INIS)
Laajalehto, Tatu; Kuosa, Maunu; Mäkilä, Tapio; Lampinen, Markku; Lahdelma, Risto
2014-01-01
Heating and cooling have a major role in the energy sector, covering 46% of total final energy use worldwide. District heating (DH) is a significant technology for improving the energy efficiency of heating systems in communities, because it enables waste heat sources to be utilised economically and therefore significantly reduces the environmental impacts of power generation. As a result of new and more stringent construction regulations for buildings, the heat demands of individual buildings are decreasing and more energy-efficient heating systems have to be developed. In this study, the energy efficiency of a new DH system which includes both a new control system called mass flow control and a new network design called a ring network is examined. A topology in the Helsinki region is studied by using a commercial DH network modelling tool, Grades Heating. The district heating network is attached to a wood-burning heat station which has a heat recovery system in use. Examination is performed by means of both technical and economic analysis. The new non-linear temperature programme that is required is adopted for supply and return temperatures, which allows greater temperature cooling and smaller flow rates. Lower district heating water temperatures are essential when reducing the heat losses in the network and heat production. Mass flow control allows smaller pressure drops in the network and thus reduces the pumping power. The aim of this study was to determine the most energy-efficient DH water supply temperatures in the case network. If the ring network design is utilised, the district heating system is easier to control. As a result the total heat consumption within the heating season is reduced compared to traditional DH systems. On the basis of the results, the new DH system is significantly more energy-efficient in the case network that was examined than the traditional design. For example, average energy losses within the constraints (which consist of heat
Maldonado, Elaina M.; Leoncikas, Vytautas; Fisher, Ciarán P.; Moore, J. Bernadette; Plant, Nick J.
2017-01-01
The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models. PMID:28782239
Topology of the Italian airport network: A scale-free small-world network with a fractal structure?
International Nuclear Information System (INIS)
Guida, Michele; Maria, Funaro
2007-01-01
In this paper, for the first time we analyze the structure of the Italian Airport Network (IAN) looking at it as a mathematical graph and investigate its topological properties. We find that it has very remarkable features, being like a scale-free network, since both the degree and the 'betweenness centrality' distributions follow a typical power-law known in literature as a Double Pareto Law. From a careful analysis of the data, the Italian Airport Network turns out to have a self-similar structure. In short, it is characterized by a fractal nature, whose typical dimensions can be easily determined from the values of the power-law scaling exponents. Moreover, we show that, according to the period examined, these distributions exhibit a number of interesting features, such as the existence of some 'hubs', i.e. in the graph theory's jargon, nodes with a very large number of links, and others most probably associated with geographical constraints. Also, we find that the IAN can be classified as a small-world network because the average distance between reachable pairs of airports grows at most as the logarithm of the number of airports. The IAN does not show evidence of 'communities' and this result could be the underlying reason behind the smallness of the value of the clustering coefficient, which is related to the probability that two nearest neighbors of a randomly chosen airport are connected
Distributed reconfigurable control strategies for switching topology networked multi-agent systems.
Gallehdari, Z; Meskin, N; Khorasani, K
2017-11-01
In this paper, distributed control reconfiguration strategies for directed switching topology networked multi-agent systems are developed and investigated. The proposed control strategies are invoked when the agents are subject to actuator faults and while the available fault detection and isolation (FDI) modules provide inaccurate and unreliable information on the estimation of faults severities. Our proposed strategies will ensure that the agents reach a consensus while an upper bound on the team performance index is ensured and satisfied. Three types of actuator faults are considered, namely: the loss of effectiveness fault, the outage fault, and the stuck fault. By utilizing quadratic and convex hull (composite) Lyapunov functions, two cooperative and distributed recovery strategies are designed and provided to select the gains of the proposed control laws such that the team objectives are guaranteed. Our proposed reconfigurable control laws are applied to a team of autonomous underwater vehicles (AUVs) under directed switching topologies and subject to simultaneous actuator faults. Simulation results demonstrate the effectiveness of our proposed distributed reconfiguration control laws in compensating for the effects of sudden actuator faults and subject to fault diagnosis module uncertainties and unreliabilities. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
A Novel Topology Link-Controlling Approach for Active Defense of a Node in a Network.
Li, Jun; Hu, HanPing; Ke, Qiao; Xiong, Naixue
2017-03-09
With the rapid development of virtual machine technology and cloud computing, distributed denial of service (DDoS) attacks, or some peak traffic, poses a great threat to the security of the network. In this paper, a novel topology link control technique and mitigation attacks in real-time environments is proposed. Firstly, a non-invasive method of deploying virtual sensors in the nodes is built, which uses the resource manager of each monitored node as a sensor. Secondly, a general topology-controlling approach of resisting the tolerant invasion is proposed. In the proposed approach, a prediction model is constructed by using copula functions for predicting the peak of a resource through another resource. The result of prediction determines whether or not to initiate the active defense. Finally, a minority game with incomplete strategy is employed to suppress attack flows and improve the permeability of the normal flows. The simulation results show that the proposed approach is very effective in protecting nodes.
A study of topologies and protocols for fiber optic local area network
Yeh, C.; Gerla, M.; Rodrigues, P.
1985-01-01
The emergence of new applications requiring high data traffic necessitates the development of high speed local area networks. Optical fiber is selected as the transmission medium due to its inherent advantages over other possible media and the dual optical bus architecture is shown to be the most suitable topology. Asynchronous access protocols, including token, random, hybrid random/token, and virtual token schemes, are developed and analyzed. Exact expressions for insertion delay and utilization at light and heavy load are derived, and intermediate load behavior is investigated by simulation. A new tokenless adaptive scheme whose control depends only on the detection of activity on the channel is shown to outperform round-robin schemes under uneven loads and multipacket traffic and to perform optimally at light load. An approximate solution to the queueing delay for an oscillating polling scheme under chaining is obtained and results are compared with simulation. Solutions to the problem of building systems with a large number of stations are presented, including maximization of the number of optical couplers, and the use of passive star/bus topologies, bridges and gateways.
Vitarelli, Michael J; Prakash, Shaurya; Talaga, David S
2011-01-15
Solid-state nanopores and nanocapillaries find increasing use in a variety of applications including DNA sequencing, synthetic nanopores, next-generation membranes for water purification, and other nanofluidic structures. This paper develops the use of electrochemical impedance spectroscopy to determine the geometry of nanocapillaries. A network equivalent circuit element is derived to include the effects of the capacitive double layer inside the nanocapillaries as well as the influence of varying nanocapillary radius. This variable topology function is similar to the finite Warburg impedance in certain limits. Analytical expressions for several different nanocapillary shapes are derived. The functions are evaluated to determine how the impedance signals will change with different nanocapillary aspect ratios and different degrees of constriction or inflation at the capillary center. Next, the complex impedance spectrum of a nanocapillary array membrane is measured at varying concentrations of electrolyte to separate the effects of nanocapillary double layer capacitance from those of nanocapillary geometry. The variable topology equivalent circuit element model of the nanocapillary is used in an equivalent circuit model that included contributions from the membrane and the measurement apparatus. The resulting values are consistent with the manufacturer's specified tolerances of the nanocapillary geometry. It is demonstrated that electrochemical impedance spectroscopy can be used as a tool for in situ determination of the geometry of nanocapillaries.
A Novel Topology Link-Controlling Approach for Active Defense of a Node in a Network
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Jun Li
2017-03-01
Full Text Available With the rapid development of virtual machine technology and cloud computing, distributed denial of service (DDoS attacks, or some peak traffic, poses a great threat to the security of the network. In this paper, a novel topology link control technique and mitigation attacks in real-time environments is proposed. Firstly, a non-invasive method of deploying virtual sensors in the nodes is built, which uses the resource manager of each monitored node as a sensor. Secondly, a general topology-controlling approach of resisting the tolerant invasion is proposed. In the proposed approach, a prediction model is constructed by using copula functions for predicting the peak of a resource through another resource. The result of prediction determines whether or not to initiate the active defense. Finally, a minority game with incomplete strategy is employed to suppress attack flows and improve the permeability of the normal flows. The simulation results show that the proposed approach is very effective in protecting nodes.
Topological and functional properties of the small GTPases protein interaction network.
Directory of Open Access Journals (Sweden)
Anna Delprato
Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.
Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney
2016-01-01
Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079
Network preservation through a topology control algorithm for wireless mesh networks
CSIR Research Space (South Africa)
Aron, FO
2008-09-01
Full Text Available Wireless mesh networks (WMNs) is becoming a promising new technology for extending coverage to farflung rural areas. This it achieves by linking the various wireless LANS (WLANs) in distant locations thus providing a vital mode complimentary...
Network topologies and dynamics leading to endotoxin tolerance and priming in innate immune cells.
Directory of Open Access Journals (Sweden)
Yan Fu
Full Text Available The innate immune system, acting as the first line of host defense, senses and adapts to foreign challenges through complex intracellular and intercellular signaling networks. Endotoxin tolerance and priming elicited by macrophages are classic examples of the complex adaptation of innate immune cells. Upon repetitive exposures to different doses of bacterial endotoxin (lipopolysaccharide or other stimulants, macrophages show either suppressed or augmented inflammatory responses compared to a single exposure to the stimulant. Endotoxin tolerance and priming are critically involved in both immune homeostasis and the pathogenesis of diverse inflammatory diseases. However, the underlying molecular mechanisms are not well understood. By means of a computational search through the parameter space of a coarse-grained three-node network with a two-stage Metropolis sampling approach, we enumerated all the network topologies that can generate priming or tolerance. We discovered three major mechanisms for priming (pathway synergy, suppressor deactivation, activator induction and one for tolerance (inhibitor persistence. These results not only explain existing experimental observations, but also reveal intriguing test scenarios for future experimental studies to clarify mechanisms of endotoxin priming and tolerance.
Automated and comprehensive link engineering supporting branched, ring, and mesh network topologies
Farina, J.; Khomchenko, D.; Yevseyenko, D.; Meester, J.; Richter, A.
2016-02-01
Link design, while relatively easy in the past, can become quite cumbersome with complex channel plans and equipment configurations. The task of designing optical transport systems and selecting equipment is often performed by an applications or sales engineer using simple tools, such as custom Excel spreadsheets. Eventually, every individual has their own version of the spreadsheet as well as their own methodology for building the network. This approach becomes unmanageable very quickly and leads to mistakes, bending of the engineering rules and installations that do not perform as expected. We demonstrate a comprehensive planning environment, which offers an efficient approach to unify, control and expedite the design process by controlling libraries of equipment and engineering methodologies, automating the process and providing the analysis tools necessary to predict system performance throughout the system and for all channels. In addition to the placement of EDFAs and DCEs, performance analysis metrics are provided at every step of the way. Metrics that can be tracked include power, CD and OSNR, SPM, XPM, FWM and SBS. Automated routine steps assist in design aspects such as equalization, padding and gain setting for EDFAs, the placement of ROADMs and transceivers, and creating regeneration points. DWDM networks consisting of a large number of nodes and repeater huts, interconnected in linear, branched, mesh and ring network topologies, can be designed much faster when compared with conventional design methods. Using flexible templates for all major optical components, our technology-agnostic planning approach supports the constant advances in optical communications.
Network Topologies and Dynamics Leading to Endotoxin Tolerance and Priming in Innate Immune Cells
Fu, Yan; Glaros, Trevor; Zhu, Meng; Wang, Ping; Wu, Zhanghan; Tyson, John; Li, Liwu; Xing, Jianhua
2012-01-01
The innate immune system, acting as the first line of host defense, senses and adapts to foreign challenges through complex intracellular and intercellular signaling networks. Endotoxin tolerance and priming elicited by macrophages are classic examples of the complex adaptation of innate immune cells. Upon repetitive exposures to different doses of bacterial endotoxin (lipopolysaccharide) or other stimulants, macrophages show either suppressed or augmented inflammatory responses compared to a single exposure to the stimulant. Endotoxin tolerance and priming are critically involved in both immune homeostasis and the pathogenesis of diverse inflammatory diseases. However, the underlying molecular mechanisms are not well understood. By means of a computational search through the parameter space of a coarse-grained three-node network with a two-stage Metropolis sampling approach, we enumerated all the network topologies that can generate priming or tolerance. We discovered three major mechanisms for priming (pathway synergy, suppressor deactivation, activator induction) and one for tolerance (inhibitor persistence). These results not only explain existing experimental observations, but also reveal intriguing test scenarios for future experimental studies to clarify mechanisms of endotoxin priming and tolerance.
Analyzing topological characteristics of neuronal functional networks in the rat brain
International Nuclear Information System (INIS)
Lu, Hu; Yang, Shengtao; Song, Yuqing; Wei, Hui
2014-01-01
In this study, we recorded spike trains from brain cortical neurons of several behavioral rats in vivo by using multi-electrode recordings. An NFN was constructed in each trial, obtaining a total of 150 NFNs in this study. The topological characteristics of NFNs were analyzed by using the two most important characteristics of complex networks, namely, small-world structure and community structure. We found that the small-world properties exist in different NFNs constructed in this study. Modular function Q was used to determine the existence of community structure in NFNs, through which we found that community-structure characteristics, which are related to recorded spike train data sets, are more evident in the Y-maze task than in the DM-GM task. Our results can also be used to analyze further the relationship between small-world characteristics and the cognitive behavioral responses of rats. - Highlights: • We constructed the neuronal function networks based on the recorded neurons. • We analyzed the two main complex network characteristics, namely, small-world structure and community structure. • NFNs which were constructed based on the recorded neurons in this study exhibit small-world properties. • Some NFNs have community structure characteristics
DEFF Research Database (Denmark)
Capaday, Charles; Ethier, C; Brizzi, L
2009-01-01
Capaday C, Ethier C, Brizzi L, Sik A, van Vreeswijk C, Gingras D. On the nature of the intrinsic connectivity of the cat motor cortex: evidence for a recurrent neural network topology. J Neurophysiol 102: 2131-2141, 2009. First published July 22, 2009; doi: 10.1152/jn.91319.2008. The details...
Horizontal and vertical growth of S. cerevisiae metabolic network.
Grassi, Luigi
2011-10-14
BACKGROUND: The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions. RESULTS: We analysed a well-studied unicellular eukaryotic organism, S. cerevisiae, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in S. cerevisiae can be dated to about 350 million years ago. CONCLUSIONS: Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the S. cerevisiae metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.
National Research Council Canada - National Science Library
Davis, Joseph A., Sr
2005-01-01
....x wireless MESH networks within a DoD Tactical network environment. Multiple sensor configurations operating with wireless MESH network technologies will be researched and analyzed for performance in expeditionary environment situations...
Slave nodes and the controllability of metabolic networks
International Nuclear Information System (INIS)
Kim, Dong-Hee; Motter, Adilson E
2009-01-01
Recent work on synthetic rescues has shown that the targeted deletion of specific metabolic genes can often be used to rescue otherwise non-viable mutants. This raises a fundamental biophysical question: to what extent can the whole-cell behavior of a large metabolic network be controlled by constraining the flux of one or more reactions in the network? This touches upon the issue of the number of degrees of freedom contained by one such network. Using the metabolic network of Escherichia coli as a model system, here we address this question theoretically by exploring not only reaction deletions, but also a continuum of all possible reaction expression levels. We show that the behavior of the metabolic network can be largely manipulated by the pinned expression of a single reaction. In particular, a relevant fraction of the metabolic reactions exhibits canalizing interactions, in that the specification of one reaction flux determines cellular growth as well as the fluxes of most other reactions in optimal steady states. The activity of individual reactions can thus be used as surrogates to monitor and possibly control cellular growth and other whole-cell behaviors. In addition to its implications for the study of control processes, our methodology provides a new approach to study how the integrated dynamics of the entire metabolic network emerges from the coordinated behavior of its component parts.
Extraversion and Neuroticism relate to topological properties of resting-state brain networks
Directory of Open Access Journals (Sweden)
Qing eGao
2013-06-01
Full Text Available With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e. betweenness centrality (BC, was positively associated with neuroticism scores in the right precentral gyrus, right caudate nucleus, right olfactory cortex and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus, indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.
Does habitat variability really promote metabolic network modularity?
Takemoto, Kazuhiro
2013-01-01
The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.
Esquivel-Gómez, J.; Arjona-Villicaña, P. D.; Acosta-Elías, J.
2015-05-01
Local processes exert influence on the growth and evolution of complex networks, which in turn shape the topological and dynamic properties of these networks. Some local processes have been researched, for example: Addition of nodes and links, rewiring of links between nodes, accelerated growth, link removal, aging, copying and multiple links prohibition. These processes impact directly into the topological and dynamical properties of complex networks. This paper introduces a new model for growth of directed complex networks which incorporates the prohibition of multiple links, addition of nodes and links, and rewiring of links. This paper also reports on the impact that these processes have in the topological properties of the networks generated with the proposed model. Numerical simulation shows that, when the frequency of rewiring increases in the proposed model, the γ exponent of the in-degree distribution approaches a value of 1.1. When the frequency of adding new links increases, the γ exponent approaches 1. That is the proposed model is able to generate all exponent values documented in real-world networks which range 1.05 < γ < 8.94.
Directory of Open Access Journals (Sweden)
Lemke Ney
2009-09-01
Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing
Structuring evolution: biochemical networks and metabolic diversification in birds.
Morrison, Erin S; Badyaev, Alexander V
2016-08-25
Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.
Rolfsson, Óttar; Paglia, Giuseppe; Magnusdóttir, Manuela; Palsson, Bernhard Ø; Thiele, Ines
2013-01-15
Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.
Cerebral energy metabolism and the brain's functional network architecture: an integrative review
Lord, Louis-David; Expert, Paul; Huckins, Jeremy F; Turkheimer, Federico E
2013-01-01
Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's ‘functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks. PMID:23756687
Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling
International Nuclear Information System (INIS)
Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh
2014-01-01
Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling
Directory of Open Access Journals (Sweden)
Amphun eChaiboonchoe
2014-12-01
Full Text Available Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The Phenotype Microarray (PM technology (Biolog, Hayward, CA, USA provides an efficient, high throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi but it has not been reported for the phenotyping of microalgae. Here we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of D-amino acids, L-dipeptides, and L-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R; Jijakli, Kenan; Salehi-Ashtiani, Kourosh
2014-01-01
Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
Network topology of stable isotope interactions in a sub-arctic raptor guild.
Dalerum, F; Hellström, P; Miranda, M; Nyström, J; Ekenstedt, J; Angerbjörn, A
2016-10-01
Predation is an ecologically important process, and intra-guild interactions may substantially influence the ecological effects of predator species. Despite a rapid expansion in the use of mathematical graph theory to describe trophic relations, network approaches have rarely been used to study interactions within predator assemblages. Assemblages of diurnal raptors are subject to substantial intra- and interspecific competition. Here we used the novel approach of applying analyzes based on network topology to species-specific data on the stable isotopes (13)C and (15)N in feathers to evaluate patterns of relative resource utilization within a guild of diurnal raptors in northern Sweden. Our guild consisted of the golden eagle (Aquila chrysaetos), the gyrfalcon (Falco rusticolus), the peregrine falcon (Falco peregrinus) and the rough-legged buzzard (Buteo lagopus). We found a modular trophic interaction structure within the guild, but the interactions were less nested than expected by chance. These results suggest low redundancy and hence a strong ecological importance of individual species. Our data also suggested that species were less connected through intra-guild interactions than expected by chance. We interpret our results as a convergence on specific isotope niches, and that body size and different hunting behaviour may mediate competition within these niches. We finally highlight that generalist predators could be ecologically important by linking specialist predator species with disparate dietary niches.
Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.
2016-01-01
Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845
Directory of Open Access Journals (Sweden)
Miao Yu
2017-01-01
Full Text Available This paper is devoted to the study of exponential synchronization problem for second-order nodes in dynamical network with time-varying communication delays and switching communication topologies. Firstly, a decomposition approach is employed to incorporate the nodes’ inertial effects into the distributed control design. Secondly, the sufficient conditions are provided to guarantee the exponential synchronization of second-order nodes in the case that the information transmission is delayed and the communication topology is balanced and arbitrarily switched. Finally, to demonstrate the effectiveness of the proposed theoretical results, it is applied to the typical second-order nodes in dynamical network, as a case study. Simulation results indicate that the proposed method has a high performance in synchronization of such network.
Directory of Open Access Journals (Sweden)
Yuanjiang Huang
2014-01-01
Full Text Available The sensor nodes in the Wireless Sensor Networks (WSNs are prone to failures due to many reasons, for example, running out of battery or harsh environment deployment; therefore, the WSNs are expected to be able to maintain network connectivity and tolerate certain amount of node failures. By applying fuzzy-logic approach to control the network topology, this paper aims at improving the network connectivity and fault-tolerant capability in response to node failures, while taking into account that the control approach has to be localized and energy efficient. Two fuzzy controllers are proposed in this paper: one is Learning-based Fuzzy-logic Topology Control (LFTC, of which the fuzzy controller is learnt from a training data set; another one is Rules-based Fuzzy-logic Topology Control (RFTC, of which the fuzzy controller is obtained through designing if-then rules and membership functions. Both LFTC and RFTC do not rely on location information, and they are localized. Comparing them with other three representative algorithms (LTRT, List-based, and NONE through extensive simulations, our two proposed fuzzy controllers have been proved to be very energy efficient to achieve desired node degree and improve the network connectivity when sensor nodes run out of battery or are subject to random attacks.
Observability of plant metabolic networks is reflected in the correlation of metabolic profiles
DEFF Research Database (Denmark)
Schwahn, Kevin; Küken, Anika; Kliebenstein, Daniel James
2016-01-01
-of-the-art genome-scale metabolic networks. By using metabolic data profiles from a set of seven environmental perturbations as well as from natural variability, we demonstrate that the data profiles of sensor metabolites are more correlated than those of nonsensor metabolites. This pattern was confirmed...
Melas, Ioannis N; Samaga, Regina; Alexopoulos, Leonidas G; Klamt, Steffen
2013-01-01
Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely
Preferential attachment in the evolution of metabolic networks
Directory of Open Access Journals (Sweden)
Elofsson Arne
2005-11-01
Full Text Available Abstract Background Many biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks. Results The connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in βγ-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment. Conclusion Here, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate
An integrated text mining framework for metabolic interaction network reconstruction
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Preecha Patumcharoenpol
2016-03-01
Full Text Available Text mining (TM in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module—MEE and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module—MINR. The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme–metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source
Signatures of arithmetic simplicity in metabolic network architecture.
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William J Riehl
2010-04-01
Full Text Available Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.
Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles
Jia, Gengjie; Stephanopoulos, Gregory; Gunawan, Rudiyanto
2012-01-01
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics. PMID:24957767
Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles
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Gengjie Jia
2012-11-01
Full Text Available Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA kinetics.
Banks, Natalie Clare; Paini, Dean Ronald; Bayliss, Kirsty Louise; Hodda, Michael
2015-02-01
More people and goods are moving further and more frequently via many different trade and transport networks under current trends of globalisation. These networks can play a major role in the unintended introduction of exotic species to new locations. With the continuing rise in global trade, more research attention is being focused on the role of networks in the spread of invasive species. This represents an emerging field of research in invasion science and the substantial knowledge being generated within other disciplines can provide ecologists with new tools with which to study invasions. For the first time, we synthesise studies from several perspectives, approaches and disciplines to derive the fundamental characteristics of network topology determining the likelihood of spread of organisms via trade and transport networks. These characteristics can be used to identify critical points of vulnerability within these networks and enable the development of more effective strategies to prevent invasions. © 2014 John Wiley & Sons Ltd/CNRS.
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Haiming Long
Full Text Available This study considers the effect of an industry's network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry's conditional-value-at-risk (CoVaR and the systemic risk contribution (ΔCoVaR using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR of panel data to analyze the relationship between network topology of the stock industry and the industry's systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust.