Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

Directory of Open Access Journals (Sweden)

Fikret Emre eKapucu

2012-06-01

Full Text Available In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC, exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing statistics based on interspike interval (ISI histograms. Moreover, the algorithm calculates interspike interval thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average and skewness of the ISI histogram. Because of the adaptive nature of the proposed algorithm, its analysis power is not limited by the type of neuronal cell network at hand. We demonstrate the functionality of our algorithm with two different types of microelectrode array (MEA data recorded from spontaneously active hESC-derived neuronal cell networks. The same data was also analyzed by two commonly employed burst detection algorithms and the differences in burst detection results are illustrated. The results demonstrate that our method is both adaptive to the firing statistics of the network and yields successful burst detection from the data. In conclusion, the proposed method is a potential tool for analyzing of hESC-derived neuronal cell networks and thus can be utilized in studies aiming to understand the development and functioning of human neuronal networks and as an analysis tool for in vitro drug screening and neurotoxicity assays.

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

Science.gov (United States)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

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Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis.

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-21

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Pluripotency gene network dynamics: System views from parametric analysis.

Science.gov (United States)

Akberdin, Ilya R; Omelyanchuk, Nadezda A; Fadeev, Stanislav I; Leskova, Natalya E; Oschepkova, Evgeniya A; Kazantsev, Fedor V; Matushkin, Yury G; Afonnikov, Dmitry A; Kolchanov, Nikolay A

2018-01-01

Multiple experimental data demonstrated that the core gene network orchestrating self-renewal and differentiation of mouse embryonic stem cells involves activity of Oct4, Sox2 and Nanog genes by means of a number of positive feedback loops among them. However, recent studies indicated that the architecture of the core gene network should also incorporate negative Nanog autoregulation and might not include positive feedbacks from Nanog to Oct4 and Sox2. Thorough parametric analysis of the mathematical model based on this revisited core regulatory circuit identified that there are substantial changes in model dynamics occurred depending on the strength of Oct4 and Sox2 activation and molecular complexity of Nanog autorepression. The analysis showed the existence of four dynamical domains with different numbers of stable and unstable steady states. We hypothesize that these domains can constitute the checkpoints in a developmental progression from naïve to primed pluripotency and vice versa. During this transition, parametric conditions exist, which generate an oscillatory behavior of the system explaining heterogeneity in expression of pluripotent and differentiation factors in serum ESC cultures. Eventually, simulations showed that addition of positive feedbacks from Nanog to Oct4 and Sox2 leads mainly to increase of the parametric space for the naïve ESC state, in which pluripotency factors are strongly expressed while differentiation ones are repressed.

A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

Science.gov (United States)

Samarasinghe, S; Ling, H

In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

Static, Dynamic and Semantic Dimensions: Towards a Multidisciplinary Approach of Social Networks Analysis

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Thovex, Christophe; Trichet, Francky

The objective of our work is to extend static and dynamic models of Social Networks Analysis (SNA), by taking conceptual aspects of enterprises and institutions social graph into account. The originality of our multidisciplinary work is to introduce abstract notions of electro-physic to define new measures in SNA, for new decision-making functions dedicated to Human Resource Management (HRM). This paper introduces a multidimensional system and new measures: (1) a tension measure for social network analysis, (2) an electrodynamic, predictive and semantic system for recommendations on social graphs evolutions and (3) a reactance measure used to evaluate the individual stress at work of the members of a social network.

Energy Efficiency Analysis for Dynamic Routing in Optical Transport Networks

DEFF Research Database (Denmark)

Vizcaíno, Jorge López; Ye, Yabin; Tafur Monroy, Idelfonso

2012-01-01

The energy efficiency in telecommunication networks is gaining more relevance as the Internet traffic is growing. The introduction of OFDM and dynamic operation opens new horizons in the operation of optical networks, improving the network flexibility and its efficiency. In this paper, we compare...... the performance in terms of energy efficiency of a flexible-grid OFDM-based solution with a fixed-grid WDM network in a dynamic scenario with time-varying connections. We highlight the benefits that the bandwidth elasticity and the flexibility of selecting different modulation formats can offer compared...

Dynamic network data envelopment analysis for university hospitals evaluation

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Maria Stella de Castro Lobo

2016-01-01

Full Text Available ABSTRACT OBJECTIVE To develop an assessment tool to evaluate the efficiency of federal university general hospitals. METHODS Data envelopment analysis, a linear programming technique, creates a best practice frontier by comparing observed production given the amount of resources used. The model is output-oriented and considers variable returns to scale. Network data envelopment analysis considers link variables belonging to more than one dimension (in the model, medical residents, adjusted admissions, and research projects. Dynamic network data envelopment analysis uses carry-over variables (in the model, financing budget to analyze frontier shift in subsequent years. Data were gathered from the information system of the Brazilian Ministry of Education (MEC, 2010-2013. RESULTS The mean scores for health care, teaching and research over the period were 58.0%, 86.0%, and 61.0%, respectively. In 2012, the best performance year, for all units to reach the frontier it would be necessary to have a mean increase of 65.0% in outpatient visits; 34.0% in admissions; 12.0% in undergraduate students; 13.0% in multi-professional residents; 48.0% in graduate students; 7.0% in research projects; besides a decrease of 9.0% in medical residents. In the same year, an increase of 0.9% in financing budget would be necessary to improve the care output frontier. In the dynamic evaluation, there was progress in teaching efficiency, oscillation in medical care and no variation in research. CONCLUSIONS The proposed model generates public health planning and programming parameters by estimating efficiency scores and making projections to reach the best practice frontier.

Intrinsic Dynamics Analysis of a DNA Octahedron by Elastic Network Model

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Guang Hu

2017-01-01

Full Text Available DNA is a fundamental component of living systems where it plays a crucial role at both functional and structural level. The programmable properties of DNA make it an interesting building block for the construction of nanostructures. However, molecular mechanisms for the arrangement of these well-defined DNA assemblies are not fully understood. In this paper, the intrinsic dynamics of a DNA octahedron has been investigated by using two types of Elastic Network Models (ENMs. The application of ENMs to DNA nanocages include the analysis of the intrinsic flexibilities of DNA double-helices and hinge sites through the calculation of the square fluctuations, as well as the intrinsic collective dynamics in terms of cross-collective map calculation coupled with global motions analysis. The dynamics profiles derived from ENMs have then been evaluated and compared with previous classical molecular dynamics simulation trajectories. The results presented here revealed that ENMs can provide useful insights into the intrinsic dynamics of large DNA nanocages and represent a useful tool in the field of structural DNA nanotechnology.

Arresting Strategy Based on Dynamic Criminal Networks Changing over Time

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Junqing Yuan

2013-01-01

Full Text Available We investigate a sequence of dynamic criminal networks on a time series based on the dynamic network analysis (DNA. According to the change of networks’ structure, networks’ variation trend is analyzed to forecast its future structure. Finally, an optimal arresting time and priority list are designed based on our analysis. Better results can be expected than that based on social network analysis (SNA.

Agent Based Modeling on Organizational Dynamics of Terrorist Network

OpenAIRE

Bo Li; Duoyong Sun; Renqi Zhu; Ze Li

2015-01-01

Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model ...

Dynamic Evolution Model Based on Social Network Services

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Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

2013-11-01

Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

Integrating atomistic molecular dynamics simulations, experiments and network analysis to study protein dynamics: strength in unity

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Elena ePapaleo

2015-05-01

Full Text Available In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

Flory-Stockmayer analysis on reprocessable polymer networks

Science.gov (United States)

Li, Lingqiao; Chen, Xi; Jin, Kailong; Torkelson, John

Reprocessable polymer networks can undergo structure rearrangement through dynamic chemistries under proper conditions, making them a promising candidate for recyclable crosslinked materials, e.g. tires. This research field has been focusing on various chemistries. However, there has been lacking of an essential physical theory explaining the relationship between abundancy of dynamic linkages and reprocessability. Based on the classical Flory-Stockmayer analysis on network gelation, we developed a similar analysis on reprocessable polymer networks to quantitatively predict the critical condition for reprocessability. Our theory indicates that it is unnecessary for all bonds to be dynamic to make the resulting network reprocessable. As long as there is no percolated permanent network in the system, the material can fully rearrange. To experimentally validate our theory, we used a thiol-epoxy network model system with various dynamic linkage compositions. The stress relaxation behavior of resulting materials supports our theoretical prediction: only 50 % of linkages between crosslinks need to be dynamic for a tri-arm network to be reprocessable. Therefore, this analysis provides the first fundamental theoretical platform for designing and evaluating reprocessable polymer networks. We thank McCormick Research Catalyst Award Fund and ISEN cluster fellowship (L. L.) for funding support.

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

Science.gov (United States)

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention–designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs. PMID:24143060

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

Science.gov (United States)

Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

2014-09-01

Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

Evolutionary dynamics of complex communications networks

CERN Document Server

Karyotis, Vasileios; Papavassiliou, Symeon

2013-01-01

Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to

Dynamics of global supply chain and electric power networks: Models, pricing analysis, and computations

Science.gov (United States)

Matsypura, Dmytro

In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the application of finite-dimensional variational inequality theory, projected dynamical systems theory, game theory, network theory, and other tools that have been recently proposed for the modeling and analysis of supply chain networks (cf. Nagurney (2006)) to electric power markets. This dissertation contributes to the extant literature on the modeling, analysis, and solution of supply chain networks, including global supply chains, in general, and electric power supply chains, in particular, in the following ways. It develops a theoretical framework for modeling, pricing analysis, and computation of electric power flows/transactions in electric power systems using the rationale for supply chain analysis. The models developed include both static and dynamic ones. The dissertation also adds a new dimension to the methodology of the theory of projected dynamical systems by proving that, irrespective of the speeds of adjustment, the equilibrium of the system remains the same. Finally, I include alternative fuel suppliers, along with their behavior into the supply chain modeling and analysis framework. This dissertation has strong practical implications. In an era in which technology and globalization, coupled with increasing risk and uncertainty, complicate electricity demand and supply within and between nations, the successful management of electric power systems and pricing become increasingly pressing topics with relevance not only for economic prosperity but also national security. This dissertation addresses such related topics by providing models, pricing tools, and algorithms for decentralized electric power supply chains. This dissertation is based heavily on the following

Discrete dynamic modeling of cellular signaling networks.

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Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Dynamic facial expressions evoke distinct activation in the face perception network: a connectivity analysis study.

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Foley, Elaine; Rippon, Gina; Thai, Ngoc Jade; Longe, Olivia; Senior, Carl

2012-02-01

Very little is known about the neural structures involved in the perception of realistic dynamic facial expressions. In the present study, a unique set of naturalistic dynamic facial emotional expressions was created. Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend Haxby et al.'s [Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. The distributed human neural system for face perception. Trends in Cognitive Science, 4, 223-233, 2000] distributed neural system for face perception. This network includes early visual regions, such as the inferior occipital gyrus, which is identified as insensitive to motion or affect but sensitive to the visual stimulus, the STS, identified as specifically sensitive to motion, and the amygdala, recruited to process affect. Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as the inferior occipital gyrus and the STS, along with coupling between the STS and the amygdala, as well as the inferior frontal gyrus. These findings support the presence of a distributed network of cortical regions that mediate the perception of different dynamic facial expressions.

Identification of Complex Dynamical Systems with Neural Networks (2/2)

CERN Multimedia

CERN. Geneva

2016-01-01

The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

Identification of Complex Dynamical Systems with Neural Networks (1/2)

CERN Multimedia

CERN. Geneva

2016-01-01

The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

Complex network analysis of phase dynamics underlying oil-water two-phase flows

Science.gov (United States)

Gao, Zhong-Ke; Zhang, Shan-Shan; Cai, Qing; Yang, Yu-Xuan; Jin, Ning-De

2016-01-01

Characterizing the complicated flow behaviors arising from high water cut and low velocity oil-water flows is an important problem of significant challenge. We design a high-speed cycle motivation conductance sensor and carry out experiments for measuring the local flow information from different oil-in-water flow patterns. We first use multivariate time-frequency analysis to probe the typical features of three flow patterns from the perspective of energy and frequency. Then we infer complex networks from multi-channel measurements in terms of phase lag index, aiming to uncovering the phase dynamics governing the transition and evolution of different oil-in-water flow patterns. In particular, we employ spectral radius and weighted clustering coefficient entropy to characterize the derived unweighted and weighted networks and the results indicate that our approach yields quantitative insights into the phase dynamics underlying the high water cut and low velocity oil-water flows. PMID:27306101

Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging.

Science.gov (United States)

Syed, Maleeha F; Lindquist, Martin A; Pillai, Jay J; Agarwal, Shruti; Gujar, Sachin K; Choe, Ann S; Caffo, Brian; Sair, Haris I

2017-12-01

Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

Dynamical analysis of yeast protein interaction network during the sake brewing process.

Science.gov (United States)

Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

2011-12-01

Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.

Complex systems and networks dynamics, controls and applications

CERN Document Server

Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu

2016-01-01

This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...

Design and implementation of dynamic hybrid Honeypot network

Science.gov (United States)

Qiao, Peili; Hu, Shan-Shan; Zhai, Ji-Qiang

2013-05-01

The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

Science.gov (United States)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral

Applications of social media and social network analysis

CERN Document Server

Kazienko, Przemyslaw

2015-01-01

This collection of contributed chapters demonstrates a wide range of applications within two overlapping research domains: social media analysis and social network analysis. Various methodologies were utilized in the twelve individual chapters including static, dynamic and real-time approaches to graph, textual and multimedia data analysis. The topics apply to reputation computation, emotion detection, topic evolution, rumor propagation, evaluation of textual opinions, friend ranking, analysis of public transportation networks, diffusion in dynamic networks, analysis of contributors to commun

Sensitivity Analysis of Dynamic Tariff Method for Congestion Management in Distribution Networks

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei; Liu, Zhaoxi

2015-01-01

The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate the congestions that might occur in a distribution network with high penetration of distribute energy resources (DERs). Sensitivity analysis of the DT method is crucial because of its decentralized...... control manner. The sensitivity analysis can obtain the changes of the optimal energy planning and thereby the line loading profiles over the infinitely small changes of parameters by differentiating the KKT conditions of the convex quadratic programming, over which the DT method is formed. Three case...

Standard representation and unified stability analysis for dynamic artificial neural network models.

Science.gov (United States)

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Analysis on the dynamic error for optoelectronic scanning coordinate measurement network

Science.gov (United States)

Shi, Shendong; Yang, Linghui; Lin, Jiarui; Guo, Siyang; Ren, Yongjie

2018-01-01

Large-scale dynamic three-dimension coordinate measurement technique is eagerly demanded in equipment manufacturing. Noted for advantages of high accuracy, scale expandability and multitask parallel measurement, optoelectronic scanning measurement network has got close attention. It is widely used in large components jointing, spacecraft rendezvous and docking simulation, digital shipbuilding and automated guided vehicle navigation. At present, most research about optoelectronic scanning measurement network is focused on static measurement capacity and research about dynamic accuracy is insufficient. Limited by the measurement principle, the dynamic error is non-negligible and restricts the application. The workshop measurement and positioning system is a representative which can realize dynamic measurement function in theory. In this paper we conduct deep research on dynamic error resources and divide them two parts: phase error and synchronization error. Dynamic error model is constructed. Based on the theory above, simulation about dynamic error is carried out. Dynamic error is quantized and the rule of volatility and periodicity has been found. Dynamic error characteristics are shown in detail. The research result lays foundation for further accuracy improvement.

Salience network dynamics underlying successful resistance of temptation

Science.gov (United States)

Nomi, Jason S; Calhoun, Vince D; Stelzel, Christine; Paschke, Lena M; Gaschler, Robert; Goschke, Thomas; Walter, Henrik; Uddin, Lucina Q

2017-01-01

Abstract Self-control and the ability to resist temptation are critical for successful completion of long-term goals. Contemporary models in cognitive neuroscience emphasize the primary role of prefrontal cognitive control networks in aligning behavior with such goals. Here, we use gaze pattern analysis and dynamic functional connectivity fMRI data to explore how individual differences in the ability to resist temptation are related to intrinsic brain dynamics of the cognitive control and salience networks. Behaviorally, individuals exhibit greater gaze distance from target location (e.g. higher distractibility) during presentation of tempting erotic images compared with neutral images. Individuals whose intrinsic dynamic functional connectivity patterns gravitate toward configurations in which salience detection systems are less strongly coupled with visual systems resist tempting distractors more effectively. The ability to resist tempting distractors was not significantly related to intrinsic dynamics of the cognitive control network. These results suggest that susceptibility to temptation is governed in part by individual differences in salience network dynamics and provide novel evidence for involvement of brain systems outside canonical cognitive control networks in contributing to individual differences in self-control. PMID:29048582

Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks.

Science.gov (United States)

Zhang, Juping; Yang, Chan; Jin, Zhen; Li, Jia

2018-07-14

In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Nonlinear dynamics analysis of a self-organizing recurrent neural network: chaos waning.

Science.gov (United States)

Eser, Jürgen; Zheng, Pengsheng; Triesch, Jochen

2014-01-01

Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.

A network dynamics approach to chemical reaction networks

NARCIS (Netherlands)

van der Schaft, Abraham; Rao, S.; Jayawardhana, B.

2016-01-01

A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a

Network dynamics with BrainX(3): a large-scale simulation of the human brain network with real-time interaction.

Science.gov (United States)

Arsiwalla, Xerxes D; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F M J

2015-01-01

BrainX(3) is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX(3) in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX(3) can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Science.gov (United States)

Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

2015-01-01

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas. PMID:25759649

Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

Directory of Open Access Journals (Sweden)

Xerxes D. Arsiwalla

2015-02-01

Full Text Available BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for real-time exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably, due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

Dynamics of subway networks based on vehicles operation timetable

Science.gov (United States)

Xiao, Xue-mei; Jia, Li-min; Wang, Yan-hui

2017-05-01

In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices' and edges' local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network.

Entropy of dynamical social networks

Science.gov (United States)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

Functional network macroscopes for probing past and present Earth system dynamics (Invited)

Science.gov (United States)

Donges, J. F.

2013-12-01

The Earth, as viewed from a physicist's perspective, is a dynamical system of great complexity. Functional complex networks are inferred from observational data and model runs or constructed on the basis of theoretical considerations. Representing statistical interdependencies or causal interactions between objects (e.g., Earth system subdomains, processes, or local field variables), functional complex networks are conceptually well-suited for naturally addressing some of the fundamental questions of Earth system analysis concerning, among others, major dynamical patterns, teleconnections, and feedback loops in the planetary machinery, as well as critical elements such as thresholds, bottlenecks, and switches. The first part of this talk concerns complex network theory and network-based time series analysis. Regarding complex network theory, the novel contributions include consistent frameworks for analyzing the topology of (i) general networks of interacting networks and (ii) networks with vertices of heterogeneously distributed weights, as well as (iii) an analytical theory for describing spatial networks. In the realm of time series analysis, (i) recurrence network analysis is put forward as a theoretically founded, nonlinear technique for the study of single, but possibly multivariate time series. (ii) Coupled climate networks are introduced as an exploratory tool of data analysis for quantitatively characterizing the intricate statistical interdependency structure within and between several fields of time series. The second part presents applications for detecting dynamical transitions (tipping points) in time series and studying bottlenecks in the atmosphere's general circulation structure. The analysis of paleoclimate data reveals a possible influence of large-scale shifts in Plio-Pleistocene African climate variability on events in human evolution. This presentation summarizes the contents of the dissertation titled "Functional network macroscopes for

Control theory of digitally networked dynamic systems

CERN Document Server

Lunze, Jan

2013-01-01

The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic

Complete synchronization on multi-layer center dynamical networks

International Nuclear Information System (INIS)

Liu Meng; Shao Yingying; Fu Xinchu

2009-01-01

In this paper, complete synchronization of three-layer center networks is studied. By using linear stability analysis approach, several different coupling schemes of three-layer center networks with the Logistic map local dynamics are discussed, and the stability conditions for synchronization are illustrated via some examples.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics

Science.gov (United States)

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Dynamic properties of epidemic spreading on finite size complex networks

Science.gov (United States)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

Science.gov (United States)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Identifying and tracking dynamic processes in social networks

Science.gov (United States)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

Directory of Open Access Journals (Sweden)

D. Hämäläinen

2008-03-01

Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

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.

Analysis of complex networks using aggressive abstraction.

Energy Technology Data Exchange (ETDEWEB)

Colbaugh, Richard; Glass, Kristin.; Willard, Gerald

2008-10-01

This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.

Tourism-planning network knowledge dynamics

DEFF Research Database (Denmark)

Dredge, Dianne

2014-01-01

This chapter explores the characteristics and functions of tourism networks as a first step in understanding how networks facilitate and reproduce knowledge. A framework to progress understandings of knowledge dynamics in tourism networks is presented that includes four key dimensions: context......, network agents, network boundaries and network resources. A case study of the development of the Next Generation Tourism Handbook (Queensland, Australia), a policy initiative that sought to bring tourism and land use planning knowledge closer together is presented. The case study illustrates...... that the tourism policy and land use planning networks operate in very different spheres and that context, network agents, network boundaries and network resources have a significant influence not only on knowledge dynamics but also on the capacity of network agents to overcome barriers to learning and to innovate....

Exploring Peer Relationships, Friendships and Group Work Dynamics in Higher Education: Applying Social Network Analysis

Science.gov (United States)

Mamas, Christoforos

2018-01-01

This study primarily applied social network analysis (SNA) to explore the relationship between friendships, peer social interactions and group work dynamics within a higher education undergraduate programme in England. A critical case study design was adopted so as to allow for an in-depth exploration of the students' voice. In doing so, the views…

Dynamic network tariffs: are they the most efficient way to match peak consumption and network incremental costs?

OpenAIRE

João Tomé Saraiva; Guido Pires; João Filipe Nunes; José Nuno Fidalgo; Rui Barbosa Pinto

2016-01-01

The purpose of this paper is to present the main results of the ongoing analysis of applying dynamic network access tariffs in Portugal. For the 2015-2017 regulatory period, the Portuguese National Regulatory Authority, ERSE, required the three main Portuguese DSOs to submit, until the end of June 2016, plans for the implementation of network dynamic tariff schemes targeting Medium, High and Extra High Voltage customers, as well as the respective cost-benefit analysis. EDP Distribuição, the m...

Study of pattern formation in multilayer adaptive network of phase oscillators in application to brain dynamics analysis

Science.gov (United States)

Kirsanov, Daniil V.; Nedaivozov, Vladimir O.; Makarov, Vladimir V.; Goremyko, Mikhail V.; Hramov, Alexander E.

2017-04-01

In the report we study the mechanisms of phase synchronization in the model of adaptive network of Kuramoto phase oscillators and discuss the possibility of the further application of the obtained results for the analysis of the neural network of brain. In our theoretical study the model network represents itself as the multilayer structure, in which the links between the elements belonging to the different layers are arranged according to the competitive rule. In order to analyze the dynamical states of the multilayer network we calculate and compare the values of local and global order parameter, which describe the degree of coherence between the neighboring nodes and the elements over whole network, respectively. We find that the global synchronous dynamics takes place for the large values of the coupling strength and are characterized by the identical topology of the interacting layers and a homogeneous distribution of the link strength within each layer. We also show that the partial (or cluster) synchronization, occurs for the small values of the coupling strength, lead to the emergence of the scale-free topology, within the layers.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Directory of Open Access Journals (Sweden)

Helmut Schmidt

2014-11-01

Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic

Social network analysis community detection and evolution

CERN Document Server

Missaoui, Rokia

2015-01-01

This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

Ensuring dynamic strategic fit of firms that compete globally in alliances and networks: proposing the Global SNA - Strategic Network Analysis - framework

Directory of Open Access Journals (Sweden)

T. Diana L. Van Aduard de Macedo-Soares

2011-02-01

Full Text Available In order to sustain their competitive advantage in the current increasingly globalized and turbulent context, more and more firms are competing globally in alliances and networks that oblige them to adopt new managerial paradigms and tools. However, their strategic analyses rarely take into account the strategic implications of these alliances and networks, considering their global relational characteristics, admittedly because of a lack of adequate tools to do so. This paper contributes to research that seeks to fill this gap by proposing the Global Strategic Network Analysis - SNA - framework. Its purpose is to help firms that compete globally in alliances and networks to carry out their strategic assessments and decision-making with a view to ensuring dynamic strategic fit from both a global and relational perspective.

Agent Based Modeling on Organizational Dynamics of Terrorist Network

Directory of Open Access Journals (Sweden)

Bo Li

2015-01-01

Full Text Available Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.

Conflict and convention in dynamic networks.

Science.gov (United States)

Foley, Michael; Forber, Patrick; Smead, Rory; Riedl, Christoph

2018-03-01

An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models. © 2017 The Author(s).

Activating and inhibiting connections in biological network dynamics

Directory of Open Access Journals (Sweden)

Knight Rob

2008-12-01

Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

Asynchronous networks: modularization of dynamics theorem

Science.gov (United States)

Bick, Christian; Field, Michael

2017-02-01

Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.

Do narcissism and emotional intelligence win us friends? : modeling dynamics of peer popularity using inferential network analysis

OpenAIRE

Czarna, Anna; Leifeld, Philip; Śmieja-Nęcka, Magdalena; Dufner, Michael; Salovey, Peter

2016-01-01

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting...

Periodic dynamics in queuing networks

Energy Technology Data Exchange (ETDEWEB)

Addabbo, Tommaso [Information Engineering Department, University of Siena, Via Roma 56, 53100 Siena (Italy)], E-mail: addabbo@dii.unisi.it; Kocarev, Ljupco [Macedonian Academy of Sciences and Arts, bul. Krste Misirkov 2, P.O. Box 428, 1000 Skopje, Republic of Macedonia (Macedonia, The Former Yugoslav Republic of)], E-mail: lkocarev@ucsd.edu

2009-08-30

This paper deals with state-dependent open Markovian (or exponential) queuing networks, for which arrival and service rates, as well as routing probabilities, may depend on the queue lengths. For a network of this kind, following Mandelbaum and Pats, we provide a formal definition of its associated fluid model, and we focus on the relationships which may occur between the network stochastic dynamics and the deterministic dynamics of its corresponding fluid model, particularly focusing on queuing networks whose fluid models have global periodic attractors.

The Dynamics of network and dyad level supply management

DEFF Research Database (Denmark)

Ellegaard, Chris

-supplier relation and its immediate network context, are presented. In analysing the data, the dynamic interdependency between management of one level and management of the other, will be demonstrated. The analysis reveals a need for an alternating approach to supply management, which takes the dynamic complexity...

The simplest problem in the collective dynamics of neural networks: is synchrony stable?

International Nuclear Information System (INIS)

Timme, Marc; Wolf, Fred

2008-01-01

For spiking neural networks we consider the stability problem of global synchrony, arguably the simplest non-trivial collective dynamics in such networks. We find that even this simplest dynamical problem—local stability of synchrony—is non-trivial to solve and requires novel methods for its solution. In particular, the discrete mode of pulsed communication together with the complicated connectivity of neural interaction networks requires a non-standard approach. The dynamics in the vicinity of the synchronous state is determined by a multitude of linear operators, in contrast to a single stability matrix in conventional linear stability theory. This unusual property qualitatively depends on network topology and may be neglected for globally coupled homogeneous networks. For generic networks, however, the number of operators increases exponentially with the size of the network. We present methods to treat this multi-operator problem exactly. First, based on the Gershgorin and Perron–Frobenius theorems, we derive bounds on the eigenvalues that provide important information about the synchronization process but are not sufficient to establish the asymptotic stability or instability of the synchronous state. We then present a complete analysis of asymptotic stability for topologically strongly connected networks using simple graph-theoretical considerations. For inhibitory interactions between dissipative (leaky) oscillatory neurons the synchronous state is stable, independent of the parameters and the network connectivity. These results indicate that pulse-like interactions play a profound role in network dynamical systems, and in particular in the dynamics of biological synchronization, unless the coupling is homogeneous and all-to-all. The concepts introduced here are expected to also facilitate the exact analysis of more complicated dynamical network states, for instance the irregular balanced activity in cortical neural networks

Revealing networks from dynamics: an introduction

International Nuclear Information System (INIS)

Timme, Marc; Casadiego, Jose

2014-01-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity. (topical review)

Analysis of the dynamic co-expression network of heart regeneration in the zebrafish

Science.gov (United States)

Rodius, Sophie; Androsova, Ganna; Götz, Lou; Liechti, Robin; Crespo, Isaac; Merz, Susanne; Nazarov, Petr V.; de Klein, Niek; Jeanty, Céline; González-Rosa, Juan M.; Muller, Arnaud; Bernardin, Francois; Niclou, Simone P.; Vallar, Laurent; Mercader, Nadia; Ibberson, Mark; Xenarios, Ioannis; Azuaje, Francisco

2016-05-01

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

Automated analysis of Physarum network structure and dynamics

Science.gov (United States)

Fricker, Mark D.; Akita, Dai; Heaton, Luke LM; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

2017-06-01

We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015.

Automated analysis of Physarum network structure and dynamics

International Nuclear Information System (INIS)

Fricker, Mark D; Heaton, Luke LM; Akita, Dai; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

2017-01-01

We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015. (paper)

On the dynamic analysis of piecewise-linear networks

OpenAIRE

Heemels, W.P.M.H.; Camlibel, M.K.; Schumacher, J.M.

2002-01-01

Piecewise-linear (PL) modeling is often used to approximate the behavior of nonlinear circuits. One of the possible PL modeling methodologies is based on the linear complementarity problem, and this approach has already been used extensively in the circuits and systems community for static networks. In this paper, the object of study will be dynamic electrical circuits that can be recast as linear complementarity systems, i.e., as interconnections of linear time-invariant differential equatio...

Dynamic and interacting complex networks

Science.gov (United States)

Dickison, Mark E.

This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

Similar but Different: Dynamic Social Network Analysis Highlights Fundamental Differences between the Fission-Fusion Societies of Two Equid Species, the Onager and Grevy's Zebra.

Directory of Open Access Journals (Sweden)

Daniel I Rubenstein

Full Text Available Understanding why animal societies take on the form that they do has benefited from insights gained by applying social network analysis to patterns of individual associations. Such analyses typically aggregate data over long time periods even though most selective forces that shape sociality have strong temporal elements. By explicitly incorporating the temporal signal in social interaction data we re-examine the network dynamics of the social systems of the evolutionarily closely-related Grevy's zebras and wild asses that show broadly similar social organizations. By identifying dynamic communities, previously hidden differences emerge: Grevy's zebras show more modularity than wild asses and in wild asses most communities consist of solitary individuals; and in Grevy's zebras, lactating females show a greater propensity to switch communities than non-lactating females and males. Both patterns were missed by static network analyses and in general, adding a temporal dimension provides insights into differences associated with the size and persistence of communities as well as the frequency and synchrony of their formation. Dynamic network analysis provides insights into the functional significance of these social differences and highlights the way dynamic community analysis can be applied to other species.

The dynamics of transmission and the dynamics of networks.

Science.gov (United States)

Farine, Damien

2017-05-01

A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors

Framework based on communicability and flow to analyze complex network dynamics

Science.gov (United States)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

Exploring the evolution of node neighborhoods in Dynamic Networks

Science.gov (United States)

Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman

2017-09-01

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

Dynamics of High-Resolution Networks

DEFF Research Database (Denmark)

Sekara, Vedran

the unprecedented amounts of information collected by mobile phones to gain detailed insight into the dynamics of social systems. This dissertation presents an unparalleled data collection campaign, collecting highly detailed traces for approximately 1000 people over the course of multiple years. The availability...... are we all affected by an ever changing network structure? Answering these questions will enrich our understanding of ourselves, our organizations, and our societies. Yet, mapping the dynamics of social networks has traditionally been an arduous undertaking. Today, however, it is possible to use...... of such dynamic maps allows us to probe the underlying social network and understand how individuals interact and form lasting friendships. More importantly, these highly detailed dynamic maps provide us new perspectives at traditional problems and allow us to quantify and predict human life....

Mean-field modeling approach for understanding epidemic dynamics in interconnected networks

International Nuclear Information System (INIS)

Zhu, Guanghu; Fu, Xinchu; Tang, Qinggan; Li, Kezan

2015-01-01

Modern systems (e.g., social, communicant, biological networks) are increasingly interconnected each other formed as ‘networks of networks’. Such complex systems usually possess inconsistent topologies and permit agents distributed in different subnetworks to interact directly/indirectly. Corresponding dynamics phenomena, such as the transmission of information, power, computer virus and disease, would exhibit complicated and heterogeneous tempo-spatial patterns. In this paper, we focus on the scenario of epidemic spreading in interconnected networks. We intend to provide a typical mean-field modeling framework to describe the time-evolution dynamics, and offer some mathematical skills to study the spreading threshold and the global stability of the model. Integrating the research with numerical analysis, we are able to quantify the effects of networks structure and epidemiology parameters on the transmission dynamics. Interestingly, we find that the diffusion transition in the whole network is governed by a unique threshold, which mainly depends on the most heterogenous connection patterns of network substructures. Further, the dynamics is highly sensitive to the critical values of cross infectivity with switchable phases.

Applying differential dynamic logic to reconfigurable biological networks.

Science.gov (United States)

Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

2017-09-01

Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Directory of Open Access Journals (Sweden)

Joshua Rodewald

2016-10-01

Full Text Available Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.

Dynamical Structure of a Traditional Amazonian Social Network

Directory of Open Access Journals (Sweden)

Paul L. Hooper

2013-11-01

Full Text Available Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’ drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.

Network analysis applications in hydrology

Science.gov (United States)

Price, Katie

2017-04-01

Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

Information governance in dynamic networked business process management

NARCIS (Netherlands)

Rasouli, M.; Eshuis, H.; Grefen, P.W.P.J.; Trienekens, J.J.M.; Kusters, R.J.

2016-01-01

Competition in today’s globalized markets forces organizations to collaborate within dynamic business networks to provide mass-customized integrated solutions for customers. The collaboration within dynamic business networks necessitates forming dynamic networked business processes (DNBPs).

Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory

Science.gov (United States)

Zhang, Lin; Lu, Jian; Zhou, Jialin; Zhu, Jinqing; Li, Yunxuan; Wan, Qian

2018-03-01

Didi Dache is the most popular taxi order mobile app in China, which provides online taxi-hailing service. The obtained big database from this app could be used to analyze the complexities’ day-to-day dynamic evolution of Didi taxi trip network (DTTN) from the level of complex network dynamics. First, this paper proposes the data cleaning and modeling methods for expressing Nanjing’s DTTN as a complex network. Second, the three consecutive weeks’ data are cleaned to establish 21 DTTNs based on the proposed big data processing technology. Then, multiple topology measures that characterize the complexities’ day-to-day dynamic evolution of these networks are provided. Third, these measures of 21 DTTNs are calculated and subsequently explained with actual implications. They are used as a training set for modeling the BP neural network which is designed for predicting DTTN complexities evolution. Finally, the reliability of the designed BP neural network is verified by comparing with the actual data and the results obtained from ARIMA method simultaneously. Because network complexities are the basis for modeling cascading failures and conducting link prediction in complex system, this proposed research framework not only provides a novel perspective for analyzing DTTN from the level of system aggregated behavior, but can also be used to improve the DTTN management level.

Discriminating lysosomal membrane protein types using dynamic neural network.

Science.gov (United States)

Tripathi, Vijay; Gupta, Dwijendra Kumar

2014-01-01

This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

Fundamental structures of dynamic social networks

DEFF Research Database (Denmark)

Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

2016-01-01

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection......, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

Directory of Open Access Journals (Sweden)

Susanne Kunkel

2017-06-01

Full Text Available NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

Studying Dynamics in Business Networks

DEFF Research Database (Denmark)

Andersen, Poul Houman; Anderson, Helen; Havila, Virpi

1998-01-01

This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland......This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland...

A dynamic discretization method for reliability inference in Dynamic Bayesian Networks

International Nuclear Information System (INIS)

Zhu, Jiandao; Collette, Matthew

2015-01-01

The material and modeling parameters that drive structural reliability analysis for marine structures are subject to a significant uncertainty. This is especially true when time-dependent degradation mechanisms such as structural fatigue cracking are considered. Through inspection and monitoring, information such as crack location and size can be obtained to improve these parameters and the corresponding reliability estimates. Dynamic Bayesian Networks (DBNs) are a powerful and flexible tool to model dynamic system behavior and update reliability and uncertainty analysis with life cycle data for problems such as fatigue cracking. However, a central challenge in using DBNs is the need to discretize certain types of continuous random variables to perform network inference while still accurately tracking low-probability failure events. Most existing discretization methods focus on getting the overall shape of the distribution correct, with less emphasis on the tail region. Therefore, a novel scheme is presented specifically to estimate the likelihood of low-probability failure events. The scheme is an iterative algorithm which dynamically partitions the discretization intervals at each iteration. Through applications to two stochastic crack-growth example problems, the algorithm is shown to be robust and accurate. Comparisons are presented between the proposed approach and existing methods for the discretization problem. - Highlights: • A dynamic discretization method is developed for low-probability events in DBNs. • The method is compared to existing approaches on two crack growth problems. • The method is shown to improve on existing methods for low-probability events

How complex a dynamical network can be?

International Nuclear Information System (INIS)

Baptista, M.S.; Kakmeni, F. Moukam; Del Magno, Gianluigi; Hussein, M.S.

2011-01-01

Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of a dynamical system. Not only they quantify how chaotic a dynamical system is, but since their sum is an upper bound for the rate of information production, they also provide a convenient way to quantify the complexity of a dynamical network. We conjecture based on numerical evidences that for a large class of dynamical networks composed by equal nodes, the sum of the positive Lyapunov exponents is bounded by the sum of all the positive Lyapunov exponents of both the synchronization manifold and its transversal directions, the last quantity being in principle easier to compute than the latter. As applications of our conjecture we: (i) show that a dynamical network composed of equal nodes and whose nodes are fully linearly connected produces more information than similar networks but whose nodes are connected with any other possible connecting topology; (ii) show how one can calculate upper bounds for the information production of realistic networks whose nodes have parameter mismatches, randomly chosen; (iii) discuss how to predict the behavior of a large dynamical network by knowing the information provided by a system composed of only two coupled nodes.

Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

Directory of Open Access Journals (Sweden)

Jose P. Perez

2014-01-01

Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

Review Essay: Does Qualitative Network Analysis Exist?

Directory of Open Access Journals (Sweden)

Rainer Diaz-Bone

2007-01-01

Full Text Available Social network analysis was formed and established in the 1970s as a way of analyzing systems of social relations. In this review the theoretical-methodological standpoint of social network analysis ("structural analysis" is introduced and the different forms of social network analysis are presented. Structural analysis argues that social actors and social relations are embedded in social networks, meaning that action and perception of actors as well as the performance of social relations are influenced by the network structure. Since the 1990s structural analysis has integrated concepts such as agency, discourse and symbolic orientation and in this way structural analysis has opened itself. Since then there has been increasing use of qualitative methods in network analysis. They are used to include the perspective of the analyzed actors, to explore networks, and to understand network dynamics. In the reviewed book, edited by Betina HOLLSTEIN and Florian STRAUS, the twenty predominantly empirically orientated contributions demonstrate the possibilities of combining quantitative and qualitative methods in network analyses in different research fields. In this review we examine how the contributions succeed in applying and developing the structural analysis perspective, and the self-positioning of "qualitative network analysis" is evaluated. URN: urn:nbn:de:0114-fqs0701287

Local Dynamics in Trained Recurrent Neural Networks.

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-23

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Local Dynamics in Trained Recurrent Neural Networks

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-01

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

A Network-Based Dynamic Analysis in an Equity Stock Market

Directory of Open Access Journals (Sweden)

Juan Eberhard

2017-01-01

Full Text Available We study how changes in the structure of a brokers’ transaction network affect the probability with which the returns and volume of the traded financial assets change significantly. We analyze how the dynamics of the brokers’ transaction network are associated with the returns and volume observed in the Chilean stock market. To do this, we construct and validate an index that synthesizes the daily changes of the brokers’ transaction network structure of equity market transactions. We find that the changes of this structure are significantly correlated with variables that describe the local and international economic-financial environments. In addition, changes in the brokers’ transaction network structure are associated with a greater probability of positive shocks of more than two standard deviations in the stock exchange index return and total traded stock volume. These results suggest that the structure of the brokers’ trading relations plays a role in determining the returns and volume of transactions in the Chilean stock market.

Temporal fidelity in dynamic social networks

DEFF Research Database (Denmark)

Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex ‘Sandy’

2015-01-01

of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution...

Visibility graph analysis on heartbeat dynamics of meditation training

Science.gov (United States)

Jiang, Sen; Bian, Chunhua; Ning, Xinbao; Ma, Qianli D. Y.

2013-06-01

We apply the visibility graph analysis to human heartbeat dynamics by constructing the complex networks of heartbeat interval time series and investigating the statistical properties of the network before and during chi and yoga meditation. The experiment results show that visibility graph analysis can reveal the dynamical changes caused by meditation training manifested as regular heartbeat, which is closely related to the adjustment of autonomous neural system, and visibility graph analysis is effective to evaluate the effect of meditation.

Pinning Synchronization of Switched Complex Dynamical Networks

Directory of Open Access Journals (Sweden)

Liming Du

2015-01-01

Full Text Available Network topology and node dynamics play a key role in forming synchronization of complex networks. Unfortunately there is no effective synchronization criterion for pinning synchronization of complex dynamical networks with switching topology. In this paper, pinning synchronization of complex dynamical networks with switching topology is studied. Two basic problems are considered: one is pinning synchronization of switched complex networks under arbitrary switching; the other is pinning synchronization of switched complex networks by design of switching when synchronization cannot achieved by using any individual connection topology alone. For the two problems, common Lyapunov function method and single Lyapunov function method are used respectively, some global synchronization criteria are proposed and the designed switching law is given. Finally, simulation results verify the validity of the results.

Interestingness-Driven Diffusion Process Summarization in Dynamic Networks

DEFF Research Database (Denmark)

Qu, Qiang; Liu, Siyuan; Jensen, Christian S.

2014-01-01

The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key...... tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which...... render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes...

Global value chains: Building blocks and network dynamics

Science.gov (United States)

Tsekeris, Theodore

2017-12-01

The paper employs measures and tools from complex network analysis to enhance the understanding and interpretation of structural characteristics pertaining to the Global Value Chains (GVCs) during the period 1995-2011. The analysis involves the country, sector and country-sector value chain networks to identify main drivers of structural change. The results indicate significant intertemporal changes, mirroring the increased globalization in terms of network size, strength and connectivity. They also demonstrate higher clustering and increased concentration of the most influential countries and country-sectors relative to all others in the GVC network, with the geographical dimension to prevail over the sectoral dimension in the formation of value chains. The regionalization and less hierarchical organization drive country-sector production sharing, while the sectoral value chain network has become more integrated and more competitive over time. The findings suggest that the impact of country-sector policies and/or shocks may vary with the own-group and network-wide influence of each country, take place in multiple geographical scales, as GVCs have a block structure, and involve time dynamics.

Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

Directory of Open Access Journals (Sweden)

Ram Prahlad T

2008-08-01

Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models

Network evolution induced by the dynamical rules of two populations

Science.gov (United States)

Platini, Thierry; Zia, R. K. P.

2010-10-01

We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (Na and Nb) and preferred degree (κa and \\kappa_b\\ll \\kappa_a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees langkbbrang and langkabrang presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal Na = Nb, the ratio of the restricted degree θ0 = langkabrang/langkbbrang appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ0 = 3.

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

Science.gov (United States)

Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

2008-12-01

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

Coupled disease-behavior dynamics on complex networks: A review

Science.gov (United States)

Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.

2015-12-01

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

Analysis of Energy Efficiency in Dynamic Optical Networks Employing Solar Energy Sources

DEFF Research Database (Denmark)

Wang, Jiayuan; Fagertun, Anna Manolova; Ruepp, Sarah Renée

2013-01-01

The paper presents energy efficient routing in dynamic optical networks, where solar energy sources are employed for the network nodes. Different parameters are evaluated, including the number of nodes that have access to solar energy sources, the different maximum solar output power, traffic type...... and the locations of solar powered nodes. Results show a maximum 39% savings in energy consumption with different increases in connection blocking probability....

Dynamic training algorithm for dynamic neural networks

International Nuclear Information System (INIS)

Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

1996-01-01

The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

Complex networks under dynamic repair model

Science.gov (United States)

Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

2018-01-01

Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

Bayesian network representing system dynamics in risk analysis of nuclear systems

Science.gov (United States)

Varuttamaseni, Athi

2011-12-01

A dynamic Bayesian network (DBN) model is used in conjunction with the alternating conditional expectation (ACE) regression method to analyze the risk associated with the loss of feedwater accident coupled with a subsequent initiation of the feed and bleed operation in the Zion-1 nuclear power plant. The use of the DBN allows the joint probability distribution to be factorized, enabling the analysis to be done on many simpler network structures rather than on one complicated structure. The construction of the DBN model assumes conditional independence relations among certain key reactor parameters. The choice of parameter to model is based on considerations of the macroscopic balance statements governing the behavior of the reactor under a quasi-static assumption. The DBN is used to relate the peak clad temperature to a set of independent variables that are known to be important in determining the success of the feed and bleed operation. A simple linear relationship is then used to relate the clad temperature to the core damage probability. To obtain a quantitative relationship among different nodes in the DBN, surrogates of the RELAP5 reactor transient analysis code are used. These surrogates are generated by applying the ACE algorithm to output data obtained from about 50 RELAP5 cases covering a wide range of the selected independent variables. These surrogates allow important safety parameters such as the fuel clad temperature to be expressed as a function of key reactor parameters such as the coolant temperature and pressure together with important independent variables such as the scram delay time. The time-dependent core damage probability is calculated by sampling the independent variables from their probability distributions and propagate the information up through the Bayesian network to give the clad temperature. With the knowledge of the clad temperature and the assumption that the core damage probability has a one-to-one relationship to it, we have

Organization of excitable dynamics in hierarchical biological networks.

Directory of Open Access Journals (Sweden)

Mark Müller-Linow

Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Network dynamics and its relationships to topology and coupling structure in excitable complex networks

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)

Robust adaptive synchronization of general dynamical networks ...

Indian Academy of Sciences (India)

Robust adaptive synchronization; dynamical network; multiple delays; multiple uncertainties. ... Networks such as neural networks, communication transmission networks, social rela- tionship networks etc. ..... a very good effect. Pramana – J.

Physical Proximity and Spreading in Dynamic Social Networks

OpenAIRE

Stopczynski, Arkadiusz; Pentland, Alex Sandy; Lehmann, Sune

2015-01-01

Most infectious diseases spread on a dynamic network of human interactions. Recent studies of social dynamics have provided evidence that spreading patterns may depend strongly on detailed micro-dynamics of the social system. We have recorded every single interaction within a large population, mapping out---for the first time at scale---the complete proximity network for a densely-connected system. Here we show the striking impact of interaction-distance on the network structure and dynamics ...

Analyzing, Modeling, and Simulation for Human Dynamics in Social Network

Directory of Open Access Journals (Sweden)

Yunpeng Xiao

2012-01-01

Full Text Available This paper studies the human behavior in the top-one social network system in China (Sina Microblog system. By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.

Decoding network dynamics in cancer

DEFF Research Database (Denmark)

Linding, Rune

2014-01-01

Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accur......Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...... and with an accuracy that parallels our characterisation of other physical systems such as Jumbo-jets. Decades of targeted molecular and biological studies have led to numerous pathway models of developmental and disease related processes. However, so far no global models have been derived from pathways, capable...

Convergent dynamics for multistable delayed neural networks

International Nuclear Information System (INIS)

Shih, Chih-Wen; Tseng, Jui-Pin

2008-01-01

This investigation aims at developing a methodology to establish convergence of dynamics for delayed neural network systems with multiple stable equilibria. The present approach is general and can be applied to several network models. We take the Hopfield-type neural networks with both instantaneous and delayed feedbacks to illustrate the idea. We shall construct the complete dynamical scenario which comprises exactly 2 n stable equilibria and exactly (3 n − 2 n ) unstable equilibria for the n-neuron network. In addition, it is shown that every solution of the system converges to one of the equilibria as time tends to infinity. The approach is based on employing the geometrical structure of the network system. Positively invariant sets and componentwise dynamical properties are derived under the geometrical configuration. An iteration scheme is subsequently designed to confirm the convergence of dynamics for the system. Two examples with numerical simulations are arranged to illustrate the present theory

Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

Directory of Open Access Journals (Sweden)

Oyang Yen-Jen

2010-10-01

Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

Structural parameter identifiability analysis for dynamic reaction networks

DEFF Research Database (Denmark)

Davidescu, Florin Paul; Jørgensen, Sten Bay

2008-01-01

method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...

Traffic Dynamics on Complex Networks: A Survey

Directory of Open Access Journals (Sweden)

Shengyong Chen

2012-01-01

Full Text Available Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

Networks dynamics in the case of emerging technologies

Energy Technology Data Exchange (ETDEWEB)

Rotolo, D

2016-07-01

This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)

Networks dynamics in the case of emerging technologies

International Nuclear Information System (INIS)

Rotolo, D

2016-01-01

This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)

Using Network Dynamical Influence to Drive Consensus

Science.gov (United States)

Punzo, Giuliano; Young, George F.; MacDonald, Malcolm; Leonard, Naomi E.

2016-05-01

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.

Science.gov (United States)

Tauste Campo, Adrià; Principe, Alessandro; Ley, Miguel; Rocamora, Rodrigo; Deco, Gustavo

2018-04-01

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.

Phosphoproteomics-based systems analysis of signal transduction networks

Directory of Open Access Journals (Sweden)

Hiroko eKozuka-Hata

2012-01-01

Full Text Available Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.

Dynamic Resource Allocation in Hybrid Access Femtocell Network

Directory of Open Access Journals (Sweden)

Afaz Uddin Ahmed

2014-01-01

Full Text Available Intercell interference is one of the most challenging issues in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduction at lower node density, but most of them are ineffective at higher node density. In this paper, a dynamic resource allocation management algorithm (DRAMA for spectrum shared hybrid access OFDMA femtocell network is proposed. To reduce the macro-femto-tier interference and to improve the quality of service, the proposed algorithm features a dynamic resource allocation scheme by controlling them both centrally and locally. The proposed scheme focuses on Femtocell Access Point (FAP owners’ satisfaction and allows maximum utilization of available resources based on congestion in the network. A simulation environment is developed to study the quantitative performance of DRAMA in hybrid access-control femtocell network and compare it to closed and open access mechanisms. The performance analysis shows that higher number of random users gets connected to the FAP without compromising FAP owners’ satisfaction allowing the macrocell to offload a large number of users in a dense heterogeneous network.

The International Trade Network: weighted network analysis and modelling

International Nuclear Information System (INIS)

Bhattacharya, K; Mukherjee, G; Manna, S S; Saramäki, J; Kaski, K

2008-01-01

Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN

How mutation alters the evolutionary dynamics of cooperation on networks

Science.gov (United States)

Ichinose, Genki; Satotani, Yoshiki; Sayama, Hiroki

2018-05-01

Cooperation is ubiquitous at every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. A recently proposed numerical metric, average gradient of selection (AGoS), a useful tool for interpreting and visualizing evolutionary dynamics on networks, allows simulation results to be visualized on a one-dimensional phase space. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we extend AGoS so that it can analyze the evolution of cooperation where mutation may alter strategies of individuals on networks. We show that our extended AGoS correctly visualizes the final states of cooperation with mutation in the individual-based simulations. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the payoff functions, fraction of cooperators, and network structures. Moreover, we found that scale-free networks are the most vulnerable to mutation and thus the dynamics of cooperation are altered from bistability to coexistence on those networks, undergoing an imperfect pitchfork bifurcation.

Learning State Space Dynamics in Recurrent Networks

Science.gov (United States)

Simard, Patrice Yvon

Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.

Modular analysis of biological networks.

Science.gov (United States)

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

Anomaly Detection in Dynamic Networks

Energy Technology Data Exchange (ETDEWEB)

Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2014-10-14

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the

Controlling the dynamics of multi-state neural networks

International Nuclear Information System (INIS)

Jin, Tao; Zhao, Hong

2008-01-01

In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks

Dynamic Intelligent Feedback Scheduling in Networked Control Systems

Directory of Open Access Journals (Sweden)

Hui-ying Chen

2013-01-01

Full Text Available For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

Dynamic Trust Management for Mobile Networks and Its Applications

Science.gov (United States)

Bao, Fenye

2013-01-01

Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…

Inferring the physical connectivity of complex networks from their functional dynamics

Directory of Open Access Journals (Sweden)

Holm Liisa

2010-05-01

background network to the observed change of the functional activities in the system. Conclusions The results presented in this study indicate a strong relationship between the structure and dynamics of complex network systems. As coupling strength increases, synchronization emerges among hub nodes and recruits small-degree nodes. The results show that the onset of global synchronization in the system hinders the reconstruction of an underlying complex structure. Our analysis helps to clarify how the synchronization is achieved in systems of different network topologies.

Adaptive-network models of collective dynamics

Science.gov (United States)

Zschaler, G.

2012-09-01

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

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....

Solving Dynamic Battlespace Movement Problems Using Dynamic Distributed Computer Networks

National Research Council Canada - National Science Library

Bradford, Robert

2000-01-01

.... The thesis designs a system using this architecture that invokes operations research network optimization algorithms to solve problems involving movement of people and equipment over dynamic road networks...

Functional asynchronous networks: Factorization of dynamics and function

Directory of Open Access Journals (Sweden)

Bick Christian

2016-01-01

Full Text Available In this note we describe the theory of functional asynchronous networks and one of the main results, the Modularization of Dynamics Theorem, which for a large class of functional asynchronous networks gives a factorization of dynamics in terms of constituent subnetworks. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network and thereby answer a question originally raised by Alon in the context of biological networks.

Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

International Nuclear Information System (INIS)

Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

2004-01-01

In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

Competing dynamic phases of active polymer networks

Science.gov (United States)

Freedman, Simon; Banerjee, Shiladitya; Dinner, Aaron R.

Recent experiments on in-vitro reconstituted assemblies of F-actin, myosin-II motors, and cross-linking proteins show that tuning local network properties can changes the fundamental biomechanical behavior of the system. For example, by varying cross-linker density and actin bundle rigidity, one can switch between contractile networks useful for reshaping cells, polarity sorted networks ideal for directed molecular transport, and frustrated networks with robust structural properties. To efficiently investigate the dynamic phases of actomyosin networks, we developed a coarse grained non-equilibrium molecular dynamics simulation of model semiflexible filaments, molecular motors, and cross-linkers with phenomenologically defined interactions. The simulation's accuracy was verified by benchmarking the mechanical properties of its individual components and collective behavior against experimental results at the molecular and network scales. By adjusting the model's parameters, we can reproduce the qualitative phases observed in experiment and predict the protein characteristics where phase crossovers could occur in collective network dynamics. Our model provides a framework for understanding cells' multiple uses of actomyosin networks and their applicability in materials research. Supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.

A natural experiment of social network formation and dynamics.

Science.gov (United States)

Phan, Tuan Q; Airoldi, Edoardo M

2015-05-26

Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.

Combination of Markov state models and kinetic networks for the analysis of molecular dynamics simulations of peptide folding.

Science.gov (United States)

Radford, Isolde H; Fersht, Alan R; Settanni, Giovanni

2011-06-09

Atomistic molecular dynamics simulations of the TZ1 beta-hairpin peptide have been carried out using an implicit model for the solvent. The trajectories have been analyzed using a Markov state model defined on the projections along two significant observables and a kinetic network approach. The Markov state model allowed for an unbiased identification of the metastable states of the system, and provided the basis for commitment probability calculations performed on the kinetic network. The kinetic network analysis served to extract the main transition state for folding of the peptide and to validate the results from the Markov state analysis. The combination of the two techniques allowed for a consistent and concise characterization of the dynamics of the peptide. The slowest relaxation process identified is the exchange between variably folded and denatured species, and the second slowest process is the exchange between two different subsets of the denatured state which could not be otherwise identified by simple inspection of the projected trajectory. The third slowest process is the exchange between a fully native and a partially folded intermediate state characterized by a native turn with a proximal backbone H-bond, and frayed side-chain packing and termini. The transition state for the main folding reaction is similar to the intermediate state, although a more native like side-chain packing is observed.

Dynamic behaviors in directed networks

International Nuclear Information System (INIS)

Park, Sung Min; Kim, Beom Jun

2006-01-01

Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed networks

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.

Directory of Open Access Journals (Sweden)

Xiaoke Ma

2015-06-01

Full Text Available Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules. We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

Identify Dynamic Network Modules with Temporal and Spatial Constraints

Energy Technology Data Exchange (ETDEWEB)

Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J

2007-09-24

Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

Science.gov (United States)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

Directory of Open Access Journals (Sweden)

István A Kovács

Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

Network Physiology: How Organ Systems Dynamically Interact

Science.gov (United States)

Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

Network Physiology: How Organ Systems Dynamically Interact.

Science.gov (United States)

Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.

Dynamical Networks Characterization of Space Weather Events

Science.gov (United States)

Orr, L.; Chapman, S. C.; Dods, J.; Gjerloev, J. W.

2017-12-01

Space weather can cause disturbances to satellite systems, impacting navigation technology and telecommunications; it can cause power loss and aviation disruption. A central aspect of the earth's magnetospheric response to space weather events are large scale and rapid changes in ionospheric current patterns. Space weather is highly dynamic and there are still many controversies about how the current system evolves in time. The recent SuperMAG initiative, collates ground-based vector magnetic field time series from over 200 magnetometers with 1-minute temporal resolution. In principle this combined dataset is an ideal candidate for quantification using dynamical networks. Network properties and parameters allow us to characterize the time dynamics of the full spatiotemporal pattern of the ionospheric current system. However, applying network methodologies to physical data presents new challenges. We establish whether a given pair of magnetometers are connected in the network by calculating their canonical cross correlation. The magnetometers are connected if their cross correlation exceeds a threshold. In our physical time series this threshold needs to be both station specific, as it varies with (non-linear) individual station sensitivity and location, and able to vary with season, which affects ground conductivity. Additionally, the earth rotates and therefore the ground stations move significantly on the timescales of geomagnetic disturbances. The magnetometers are non-uniformly spatially distributed. We will present new methodology which addresses these problems and in particular achieves dynamic normalization of the physical time series in order to form the network. Correlated disturbances across the magnetometers capture transient currents. Once the dynamical network has been obtained [1][2] from the full magnetometer data set it can be used to directly identify detailed inferred transient ionospheric current patterns and track their dynamics. We will show

A dynamic Bayesian network based approach to safety decision support in tunnel construction

International Nuclear Information System (INIS)

Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying

2015-01-01

This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

Model and Dynamic Behavior of Malware Propagation over Wireless Sensor Networks

Science.gov (United States)

Song, Yurong; Jiang, Guo-Ping

Based on the inherent characteristics of wireless sensor networks (WSN), the dynamic behavior of malware propagation in flat WSN is analyzed and investigated. A new model is proposed using 2-D cellular automata (CA), which extends the traditional definition of CA and establishes whole transition rules for malware propagation in WSN. Meanwhile, the validations of the model are proved through theoretical analysis and simulations. The theoretical analysis yields closed-form expressions which show good agreement with the simulation results of the proposed model. It is shown that the malware propaga-tion in WSN unfolds neighborhood saturation, which dominates the effects of increasing infectivity and limits the spread of the malware. MAC mechanism of wireless sensor networks greatly slows down the speed of malware propagation and reduces the risk of large-scale malware prevalence in these networks. The proposed model can describe accurately the dynamic behavior of malware propagation over WSN, which can be applied in developing robust and efficient defense system on WSN.

Complex networks: Dynamics and security

Indian Academy of Sciences (India)

This paper presents a perspective in the study of complex networks by focusing on how dynamics may affect network security under attacks. ... Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona 85287, USA; Institute of Mathematics and Computer Science, University of Sao Paulo, Brazil ...

Learning dynamic Bayesian networks with mixed variables

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

Multiscale analysis of spreading in a large communication network

International Nuclear Information System (INIS)

Kivelä, Mikko; Pan, Raj Kumar; Kaski, Kimmo; Kertész, János; Saramäki, Jari; Karsai, Márton

2012-01-01

In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how a dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and a susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large-scale time-stamped data on mobile phone calls, we extend earlier results that indicate the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multiscale analysis and pinpoint the importance of the timings of event sequences on individual links, their correlations with neighboring sequences, and the temporal pathways taken by the network-scale spreading process. This is achieved by studying empirically and analytically different characteristic relay times of links, relevant to the respective scales, and a set of temporal reference models that allow for removing selected time-domain correlations one by one. Our analysis shows that for the spreading velocity, time-domain inhomogeneities are as important as the network topology, which indicates the need to take time-domain information into account when studying spreading dynamics. In particular, results for the different characteristic relay times underline the importance of the burstiness of individual links

Dynamic defense and network randomization for computer systems

Science.gov (United States)

Chavez, Adrian R.; Stout, William M. S.; Hamlet, Jason R.; Lee, Erik James; Martin, Mitchell Tyler

2018-05-29

The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissance stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization. Network parameters that can be randomized include IP addresses, application port numbers, paths data packets navigate through the network, application randomization, etc.

Self-organization of complex networks as a dynamical system.

Science.gov (United States)

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

Science.gov (United States)

Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

Directory of Open Access Journals (Sweden)

Jihoon Park

Full Text Available Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random with a musculoskeletal model (i.e., a snake-like robot as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1 the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2 two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Nonlinear Dynamics on Interconnected Networks

Science.gov (United States)

Arenas, Alex; De Domenico, Manlio

2016-06-01

Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).

Dynamic capabilities and network benefits

Directory of Open Access Journals (Sweden)

Helge Svare

2017-01-01

Full Text Available The number of publicly funded initiatives to establish or strengthen networks and clusters, in order to enhance innovation, has been increasing. Returns on such investments vary, and the aim of this study is to explore to what extent the variation in benefits for firms participating in networks or clusters can be explained by their dynamic capabilities (DC. Based on survey data from five Norwegian networks, the results suggest that firms with higher DC are more successful in harvesting the potential benefits of being member of a network.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

Science.gov (United States)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

Dynamical Interplay between Awareness and Epidemic Spreading in Multiplex Networks

Science.gov (United States)

Granell, Clara; Gómez, Sergio; Arenas, Alex

2013-09-01

We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

Dynamical interplay between awareness and epidemic spreading in multiplex networks.

Science.gov (United States)

Granell, Clara; Gómez, Sergio; Arenas, Alex

2013-09-20

We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

Dynamic characteristics of a cyclic-periodic structure with a piezoelectric network

Directory of Open Access Journals (Sweden)

Li Lin

2015-10-01

Full Text Available This paper deals with a cyclic-periodic structure with a piezoelectric network. In such a system, there is not only mechanical connection but also electrical connection between adjacent periodic sectors. The objective is to learn whether the presence of a piezoelectric network would change the dynamic characteristics of the system. The background of the research is about vibration reduction of a bladed disk in an aero-engine, and the system is simulated by a lumped parameter model. The dynamic equations of the system are derived, and then the analytical solution corresponding to the eigenvalue problem is given. The vibration responses to single traveling wave excitations (EO excitations and multiple traveling wave excitations (NEO excitations are studied. The results show that the presence of a piezoelectric network would change the natural frequencies of the system compared with those of the system with the piezoelectric shunt circuit. The forced response is sensitive to the connection type and the elements of the network. An energy analysis of the electro-mechanical coupling system has been performed to understand its dynamic behavior, and the following conclusion is obtained: a vibration reduction to excitations whose primary harmonic component is not zero can be achieved by a parallel piezoelectric network, while a reduction to other excitations should be based on a series piezoelectric network.

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation.

Science.gov (United States)

Jablonski, Piotr; Poe, Gina; Zochowski, Michal

2007-03-01

The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

Quantifying the dynamics of coupled networks of switches and oscillators.

Directory of Open Access Journals (Sweden)

Matthew R Francis

Full Text Available Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems.

Synthesis of recurrent neural networks for dynamical system simulation.

Science.gov (United States)

Trischler, Adam P; D'Eleuterio, Gabriele M T

2016-08-01

We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamics of functional failures and recovery in complex road networks

Science.gov (United States)

Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.

2017-11-01

We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

Recovery time after localized perturbations in complex dynamical networks

International Nuclear Information System (INIS)

Mitra, Chiranjit; Kittel, Tim; Kurths, Jürgen; Donner, Reik V; Choudhary, Anshul

2017-01-01

Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed

Recovery time after localized perturbations in complex dynamical networks

Science.gov (United States)

Mitra, Chiranjit; Kittel, Tim; Choudhary, Anshul; Kurths, Jürgen; Donner, Reik V.

2017-10-01

Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed

Climate dynamics: a network-based approach for the analysis of global precipitation.

Science.gov (United States)

Scarsoglio, Stefania; Laio, Francesco; Ridolfi, Luca

2013-01-01

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can

Climate dynamics: a network-based approach for the analysis of global precipitation.

Directory of Open Access Journals (Sweden)

Stefania Scarsoglio

Full Text Available Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010. The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that

Transient analysis for PWR reactor core using neural networks predictors

International Nuclear Information System (INIS)

Gueray, B.S.

2001-01-01

In this study, transient analysis for a Pressurized Water Reactor core has been performed. A lumped parameter approximation is preferred for that purpose, to describe the reactor core together with mechanism which play an important role in dynamic analysis. The dynamic behavior of the reactor core during transients is analyzed considering the transient initiating events, wich are an essential part of Safety Analysis Reports. several transients are simulated based on the employed core model. Simulation results are in accord the physical expectations. A neural network is developed to predict the future response of the reactor core, in advance. The neural network is trained using the simulation results of a number of representative transients. Structure of the neural network is optimized by proper selection of transfer functions for the neurons. Trained neural network is used to predict the future responses following an early observation of the changes in system variables. Estimated behaviour using the neural network is in good agreement with the simulation results for various for types of transients. Results of this study indicate that the designed neural network can be used as an estimator of the time dependent behavior of the reactor core under transient conditions

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

Science.gov (United States)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Cognitive radio networks dynamic resource allocation schemes

CERN Document Server

Wang, Shaowei

2014-01-01

This SpringerBrief presents a survey of dynamic resource allocation schemes in Cognitive Radio (CR) Systems, focusing on the spectral-efficiency and energy-efficiency in wireless networks. It also introduces a variety of dynamic resource allocation schemes for CR networks and provides a concise introduction of the landscape of CR technology. The author covers in detail the dynamic resource allocation problem for the motivations and challenges in CR systems. The Spectral- and Energy-Efficient resource allocation schemes are comprehensively investigated, including new insights into the trade-off

Robustness analysis of uncertain dynamical neural networks with multiple time delays.

Science.gov (United States)

Senan, Sibel

2015-10-01

This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamics-based centrality for directed networks.

Science.gov (United States)

Masuda, Naoki; Kori, Hiroshi

2010-11-01

Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

Science.gov (United States)

Serçinoglu, Onur; Ozbek, Pemra

2018-05-25

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

Critical dynamics in associative memory networks

Directory of Open Access Journals (Sweden)

Maximilian eUhlig

2013-07-01

Full Text Available Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network endowed with Hebbian learning only does not allow for simultaneous information storage and criticality. However, the critical regime is can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity.

Network dynamics in the healthy and epileptic developing brain

Directory of Open Access Journals (Sweden)

Richard Rosch

2018-03-01

Full Text Available Electroencephalography (EEG allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months. We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

Science.gov (United States)

Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

2017-09-01

In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

Exponential Synchronization of Uncertain Complex Dynamical Networks with Delay Coupling

International Nuclear Information System (INIS)

Wang Lifu; Kong Zhi; Jing Yuanwei

2010-01-01

This paper studies the global exponential synchronization of uncertain complex delayed dynamical networks. The network model considered is general dynamical delay networks with unknown network structure and unknown coupling functions but bounded. Novel delay-dependent linear controllers are designed via the Lyapunov stability theory. Especially, it is shown that the controlled networks are globally exponentially synchronized with a given convergence rate. An example of typical dynamical network of this class, having the Lorenz system at each node, has been used to demonstrate and verify the novel design proposed. And, the numerical simulation results show the effectiveness of proposed synchronization approaches. (general)

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

Science.gov (United States)

Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

2017-09-08

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

Science.gov (United States)

Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

2017-09-01

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

A network-base analysis of CMIP5 "historical" experiments

Science.gov (United States)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

Directory of Open Access Journals (Sweden)

Yao Junyang

2014-06-01

Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

A Dynamic Network Approach to the Assessment of Terrorist Groups and the Impact of Alternative Courses of Action

National Research Council Canada - National Science Library

Carley, Kathleen M

2006-01-01

Dynamic network analysis (DNA) is an emergent field centered on the collection, analysis, understanding and prediction of dynamic relations among various entities such as actors, events and resources and the impact...

Do Narcissism and Emotional Intelligence Win Us Friends? Modeling Dynamics of Peer Popularity Using Inferential Network Analysis.

Science.gov (United States)

Czarna, Anna Z; Leifeld, Philip; Śmieja, Magdalena; Dufner, Michael; Salovey, Peter

2016-09-27

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity. © 2016 by the Society for Personality and Social Psychology, Inc.

Modeling Insurgent Network Structure and Dynamics

Science.gov (United States)

Gabbay, Michael; Thirkill-Mackelprang, Ashley

2010-03-01

We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.

Dynamic motifs in socio-economic networks

Science.gov (United States)

Zhang, Xin; Shao, Shuai; Stanley, H. Eugene; Havlin, Shlomo

2014-12-01

Socio-economic networks are of central importance in economic life. We develop a method of identifying and studying motifs in socio-economic networks by focusing on “dynamic motifs,” i.e., evolutionary connection patterns that, because of “node acquaintances” in the network, occur much more frequently than random patterns. We examine two evolving bi-partite networks: i) the world-wide commercial ship chartering market and ii) the ship build-to-order market. We find similar dynamic motifs in both bipartite networks, even though they describe different economic activities. We also find that “influence” and “persistence” are strong factors in the interaction behavior of organizations. When two companies are doing business with the same customer, it is highly probable that another customer who currently only has business relationship with one of these two companies, will become customer of the second in the future. This is the effect of influence. Persistence means that companies with close business ties to customers tend to maintain their relationships over a long period of time.

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

Science.gov (United States)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1997-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

A mathematical programming approach for sequential clustering of dynamic networks

Science.gov (United States)

Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

2016-02-01

A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

Transient stability analysis of a distribution network with distributed generators

NARCIS (Netherlands)

Xyngi, I.; Ishchenko, A.; Popov, M.; Sluis, van der L.

2009-01-01

This letter describes the transient stability analysis of a 10-kV distribution network with wind generators, microturbines, and CHP plants. The network being modeled in Matlab/Simulink takes into account detailed dynamic models of the generators. Fault simulations at various locations are

Network evolution induced by the dynamical rules of two populations

International Nuclear Information System (INIS)

Platini, Thierry; Zia, R K P

2010-01-01

We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (N a and N b ) and preferred degree (κ a and κ b a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees (k bb ) and (k ab ) presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal N a = N b , the ratio of the restricted degree θ 0 = (k ab )/(k bb ) appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t 1 = κ b ) the total number of links presents a linear evolution, where the two populations are indistinguishable and where θ 0 = 1. Interestingly, in the intermediate time regime (defined for t 1 2 ∝κ a and for which θ 0 = 5), the system reaches a transient stationary state, where the number of contacts among introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ 0 = 3

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

Science.gov (United States)

Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

2017-01-01

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

Resumption of dynamism in damaged networks of coupled oscillators

Science.gov (United States)

Kundu, Srilena; Majhi, Soumen; Ghosh, Dibakar

2018-05-01

Deterioration in dynamical activities may come up naturally or due to environmental influences in a massive portion of biological and physical systems. Such dynamical degradation may have outright effect on the substantive network performance. This requires us to provide some proper prescriptions to overcome undesired circumstances. In this paper, we present a scheme based on external feedback that can efficiently revive dynamism in damaged networks of active and inactive oscillators and thus enhance the network survivability. Both numerical and analytical investigations are performed in order to verify our claim. We also provide a comparative study on the effectiveness of this mechanism for feedbacks to the inactive group or to the active group only. Most importantly, resurrection of dynamical activity is realized even in time-delayed damaged networks, which are considered to be less persistent against deterioration in the form of inactivity in the oscillators. Furthermore, prominence in our approach is substantiated by providing evidence of enhanced network persistence in complex network topologies taking small-world and scale-free architectures, which makes the proposed remedy quite general. Besides the study in the network of Stuart-Landau oscillators, affirmative influence of external feedback has been justified in the network of chaotic Rössler systems as well.

The Social Dynamics of Innovation Networks

NARCIS (Netherlands)

Rutten, Roel; Benneworth, Paul Stephen; Irawati, Dessy; Boekema, Frans

2014-01-01

The social dynamics of innovation networks captures the important role of trust, social capital, institutions and norms and values in the creation of knowledge in innovation networks. In doing so, this book connects to a long-standing debate on the socio-spatial context of innovation in economic

Dynamical Adaptation in Terrorist Cells/Networks

DEFF Research Database (Denmark)

Hussain, Dil Muhammad Akbar; Ahmed, Zaki

2010-01-01

Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...

Nonlinear analysis of gas-water/oil-water two-phase flow in complex networks

CERN Document Server

Gao, Zhong-Ke; Wang, Wen-Xu

2014-01-01

Understanding the dynamics of multi-phase flows has been a challenge in the fields of nonlinear dynamics and fluid mechanics. This chapter reviews our work on two-phase flow dynamics in combination with complex network theory. We systematically carried out gas-water/oil-water two-phase flow experiments for measuring the time series of flow signals which is studied in terms of the mapping from time series to complex networks. Three network mapping methods were proposed for the analysis and identification of flow patterns, i.e. Flow Pattern Complex Network (FPCN), Fluid Dynamic Complex Network (FDCN) and Fluid Structure Complex Network (FSCN). Through detecting the community structure of FPCN based on K-means clustering, distinct flow patterns can be successfully distinguished and identified. A number of FDCN’s under different flow conditions were constructed in order to reveal the dynamical characteristics of two-phase flows. The FDCNs exhibit universal power-law degree distributions. The power-law exponent ...

Asymmetrically extremely dilute neural networks with Langevin dynamics and unconventional results

International Nuclear Information System (INIS)

Hatchett, J P L; Coolen, A C C

2004-01-01

We study graded response attractor neural networks with asymmetrically extremely dilute interactions and Langevin dynamics. We solve our model in the thermodynamic limit using generating functional analysis, and find (in contrast to the binary neurons case) that even in statics, for T > 0 or large α, one cannot eliminate the non-persistent order parameters, atypically for recurrent neural network models. The macroscopic dynamics is driven by the (non-trivial) joint distribution of neurons and fields, rather than just the (Gaussian) field distribution. We calculate phase transition lines and find, as may be expected for this asymmetric model, that there is no spin-glass phase, only recall and paramagnetic phases. We present simulation results in support of our theory

Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

Directory of Open Access Journals (Sweden)

Ayman Hamdy Kassem

2011-01-01

Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

Clustering promotes switching dynamics in networks of noisy neurons

Science.gov (United States)

Franović, Igor; Klinshov, Vladimir

2018-02-01

Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

Dynamic safety assessment of natural gas stations using Bayesian network

Energy Technology Data Exchange (ETDEWEB)

Zarei, Esmaeil, E-mail: smlzarei65@gmail.com [Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan (Iran, Islamic Republic of); Azadeh, Ali [School of Industrial and Systems Engineering, Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran (Iran, Islamic Republic of); Khakzad, Nima [Safety and Security Science Section, Delft University of Technology, Delft (Netherlands); Aliabadi, Mostafa Mirzaei [Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan (Iran, Islamic Republic of); Mohammadfam, Iraj, E-mail: mohammadfam@umsha.ac.ir [Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan (Iran, Islamic Republic of)

2017-01-05

Graphical abstract: Dynamic cause-consequence analysis of the regulator system failure using BN. - Highlights: • A dynamic and comprehensive QRA (DCQRA) framework is proposed for safety assessment of CGSs. • Bow-tie diagram and Bayesian network are employed for accident scenario modeling. • Critical basic events and minimal cut sets are identified using probability updating. - Abstract: Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.

Dynamic safety assessment of natural gas stations using Bayesian network

International Nuclear Information System (INIS)

Zarei, Esmaeil; Azadeh, Ali; Khakzad, Nima; Aliabadi, Mostafa Mirzaei; Mohammadfam, Iraj

2017-01-01

Graphical abstract: Dynamic cause-consequence analysis of the regulator system failure using BN. - Highlights: • A dynamic and comprehensive QRA (DCQRA) framework is proposed for safety assessment of CGSs. • Bow-tie diagram and Bayesian network are employed for accident scenario modeling. • Critical basic events and minimal cut sets are identified using probability updating. - Abstract: Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.

A neural network approach to the study of dynamics and structure of molecular systems

International Nuclear Information System (INIS)

Getino, C.; Sumpter, B.G.; Noid, D.W.

1994-01-01

Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.

Science.gov (United States)

Baur, Brittany; Bozdag, Serdar

2015-04-01

One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

Modelling flow dynamics in water distribution networks using ...

African Journals Online (AJOL)

One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

Structure-Activity Relationship in TLR4 Mutations: Atomistic Molecular Dynamics Simulations and Residue Interaction Network Analysis

Science.gov (United States)

Anwar, Muhammad Ayaz; Choi, Sangdun

2017-03-01

Toll-like receptor 4 (TLR4), a vital innate immune receptor present on cell surfaces, initiates a signaling cascade during danger and bacterial intrusion. TLR4 needs to form a stable hexamer complex, which is necessary to dimerize the cytoplasmic domain. However, D299G and T399I polymorphism may abrogate the stability of the complex, leading to compromised TLR4 signaling. Crystallography provides valuable insights into the structural aspects of the TLR4 ectodomain; however, the dynamic behavior of polymorphic TLR4 is still unclear. Here, we employed molecular dynamics simulations (MDS), as well as principal component and residue network analyses, to decipher the structural aspects and signaling propagation associated with mutations in TLR4. The mutated complexes were less cohesive, displayed local and global variation in the secondary structure, and anomalous decay in rotational correlation function. Principal component analysis indicated that the mutated complexes also exhibited distinct low-frequency motions, which may be correlated to the differential behaviors of these TLR4 variants. Moreover, residue interaction networks (RIN) revealed that the mutated TLR4/myeloid differentiation factor (MD) 2 complex may perpetuate abnormal signaling pathways. Cumulatively, the MDS and RIN analyses elucidated the mutant-specific conformational alterations, which may help in deciphering the mechanism of loss-of-function mutations.

Advances in dynamic network modeling in complex transportation systems

CERN Document Server

Ukkusuri, Satish V

2013-01-01

This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.

Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

Directory of Open Access Journals (Sweden)

Brendan Chambers

2016-08-01

Full Text Available Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.

D2D-Enabled Small Cell Network Control Scheme Based on the Dynamic Stackelberg Game

Directory of Open Access Journals (Sweden)

Sungwook Kim

2017-01-01

Full Text Available For current and future cellular networks, small cell structure with licensed and unlicensed bandwidth, caching content provisioning, and device-to-device (D2D communications is seen as a necessary architecture. Recently, a series of control methods have been developed to address a myriad of challenges in next-generation small cell networks. In this study, we focus on the design of novel D2D-enabled small cell network control scheme by allowing caching and unlicensed D2D communications. Motivated by game theory and learning algorithm, the proposed scheme adaptively selects caching contents and splits the available bandwidth for licensed and unlicensed communications. Under dynamically changing network environments, we capture the dynamics of the network system and design a new dynamic Stackelberg game model. Based on a hierarchical and feedback based control manner, small base stations and users can be leaders or followers dynamically while improving 5G network performance. Simulations and performance analysis verify the efficiency of the proposed scheme, showing that our approach can outperform existing schemes by about 5%~15% in terms of bandwidth utilization, cache hit ratio, and system throughput.

NAPS: Network Analysis of Protein Structures

Science.gov (United States)

Chakrabarty, Broto; Parekh, Nita

2016-01-01

Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

NET-2 Network Analysis Program

International Nuclear Information System (INIS)

Malmberg, A.F.

1974-01-01

The NET-2 Network Analysis Program is a general purpose digital computer program which solves the nonlinear time domain response and the linearized small signal frequency domain response of an arbitrary network of interconnected components. NET-2 is capable of handling a variety of components and has been applied to problems in several engineering fields, including electronic circuit design and analysis, missile flight simulation, control systems, heat flow, fluid flow, mechanical systems, structural dynamics, digital logic, communications network design, solid state device physics, fluidic systems, and nuclear vulnerability due to blast, thermal, gamma radiation, neutron damage, and EMP effects. Network components may be selected from a repertoire of built-in models or they may be constructed by the user through appropriate combinations of mathematical, empirical, and topological functions. Higher-level components may be defined by subnetworks composed of any combination of user-defined components and built-in models. The program provides a modeling capability to represent and intermix system components on many levels, e.g., from hole and electron spatial charge distributions in solid state devices through discrete and integrated electronic components to functional system blocks. NET-2 is capable of simultaneous computation in both the time and frequency domain, and has statistical and optimization capability. Network topology may be controlled as a function of the network solution. (U.S.)

Actin dynamics and the elasticity of cytoskeletal networks

Directory of Open Access Journals (Sweden)

2009-09-01

Full Text Available The structural integrity of a cell depends on its cytoskeleton, which includes an actin network. This network is transient and depends upon the continual polymerization and depolymerization of actin. The degradation of an actin network, and a corresponding reduction in cell stiffness, can indicate the presence of disease. Numerical simulations will be invaluable for understanding the physics of these systems and the correlation between actin dynamics and elasticity. Here we develop a model that is capable of generating actin network structures. In particular, we develop a model of actin dynamics which considers the polymerization, depolymerization, nucleation, severing, and capping of actin filaments. The structures obtained are then fed directly into a mechanical model. This allows us to qualitatively assess the effects of changing various parameters associated with actin dynamics on the elasticity of the material.

Dynamic Data-Driven UAV Network for Plume Characterization

Science.gov (United States)

2016-05-23

AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0090 5c.  PROGRAM ELEMENT...studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles ( UAVs ) or micro/miniature aerial

Dynamical community structure of populations evolving on genotype networks

International Nuclear Information System (INIS)

Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

2015-01-01

Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

Structure-based control of complex networks with nonlinear dynamics.

Science.gov (United States)

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Analysis of Social Network Dynamics with Models from the Theory of Complex Adaptive Systems

OpenAIRE

Lymperopoulos , Ilias; Lekakos , George

2013-01-01

Part 4: Protocols, Regulation and Social Networking; International audience; The understanding and modeling of social dynamics in a complex and unpredictable world, emerges as a research target of particular importance. Success in this direction can yield valuable knowledge as to how social phenomena form and evolve in varying socioeconomic contexts comprising economic crises, societal disasters, cultural differences and security threats among others. The study of social dynamics occurring in...

Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

Directory of Open Access Journals (Sweden)

Laila Hasmi

2017-11-01

present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence.

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

Science.gov (United States)

Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

2015-12-03

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Chunyang Lei

2015-12-01

Full Text Available Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT, Machine-to-Machine (M2M communications, Vehicular-to-Vehicular (V2V communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

Adaptive control of dynamical synchronization on evolving networks with noise disturbances

Science.gov (United States)

Yuan, Wu-Jie; Zhou, Jian-Fang; Sendiña-Nadal, Irene; Boccaletti, Stefano; Wang, Zhen

2018-02-01

In real-world networked systems, the underlying structure is often affected by external and internal unforeseen factors, making its evolution typically inaccessible. An adaptive strategy was introduced for maintaining synchronization on unpredictably evolving networks [Sorrentino and Ott, Phys. Rev. Lett. 100, 114101 (2008), 10.1103/PhysRevLett.100.114101], which yet does not consider the noise disturbances widely existing in networks' environments. We provide here strategies to control dynamical synchronization on slowly and unpredictably evolving networks subjected to noise disturbances which are observed at the node and at the communication channel level. With our strategy, the nodes' coupling strength is adaptively adjusted with the aim of controlling synchronization, and according only to their received signal and noise disturbances. We first provide a theoretical analysis of the control scheme by introducing an error potential function to seek for the minimization of the synchronization error. Then, we show numerical experiments which verify our theoretical results. In particular, it is found that our adaptive strategy is effective even for the case in which the dynamics of the uncontrolled network would be explosive (i.e., the states of all the nodes would diverge to infinity).

Dynamic social networks based on movement

Science.gov (United States)

Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.

2016-01-01

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.

Dynamic Frequency Control in Power Networks

OpenAIRE

Zhao, Changhong; Mallada Garcia, Enrique; Low, Steven H.

2016-01-01

Node controllers in power distribution networks in accordance with embodiments of the invention enable dynamic frequency control. One embodiment includes a node controller comprising a network interface a processor; and a memory containing a frequency control application; and a plurality of node operating parameters describing the operating parameters of a node, where the node is selected from a group consisting of at least one generator node in a power distribution network wherein the proces...

Network evolution driven by dynamics applied to graph coloring

International Nuclear Information System (INIS)

Wu Jian-She; Li Li-Guang; Yu Xin; Jiao Li-Cheng; Wang Xiao-Hua

2013-01-01

An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring

Dynamic network expansion, contraction, and connectivity in the river corridor of mountain stream network

Science.gov (United States)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.

2017-12-01

River networks are broadly recognized to expand and contract in response to hydrologic forcing. Additionally, the individual controls on river corridor dynamics of hydrologic forcing and geologic setting are well recognized. However, we currently lack tools to integrate our understanding of process dynamics in the river corridor and make predictions at the scale of river networks. In this study, we develop a perceptual model of the river corridor in mountain river networks, translate this into a reduced-complexity mechanistic model, and implement the model in a well-studied headwater catchment. We found that the river network was most sensitive to hydrologic dynamics under the lowest discharges (Qgauge managers of water resources who need to estimate connectivity and flow initiation location along the river corridor over broad, unstudied catchments.

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks.

Science.gov (United States)

Perisic, Ana; Bauch, Chris T

2009-05-28

Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. We simulate transmission of a vaccine-preventable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks

Directory of Open Access Journals (Sweden)

Bauch Chris T

2009-05-01

Full Text Available Abstract Background Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. Methods We simulate transmission of a vaccine-prevetable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. Results We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination; large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination, and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination. We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. Conclusion For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large

Cluster analysis of word frequency dynamics

Science.gov (United States)

Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

2015-01-01

This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

Cluster analysis of word frequency dynamics

International Nuclear Information System (INIS)

Maslennikova, Yu S; Bochkarev, V V; Belashova, I A

2015-01-01

This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations

Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance.

Science.gov (United States)

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.

Applications of flow-networks to opinion-dynamics

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Tupikina, Liubov; Kurths, Jürgen

2015-04-01

Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging.

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Patel, Tapan P; Man, Karen; Firestein, Bonnie L; Meaney, David F

2015-03-30

Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s-1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. Copyright © 2015. Published by Elsevier B.V.

Generalized master equations for non-Poisson dynamics on networks.

Science.gov (United States)

Hoffmann, Till; Porter, Mason A; Lambiotte, Renaud

2012-10-01

The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed by Poisson processes, which is not typically the case in empirical temporal networks. Accordingly, we examine the effects of non-Poisson inter-event statistics on the dynamics of edges, and we apply the concept of a generalized master equation to the study of continuous-time random walks on networks. We show that this equation reduces to the standard rate equations when the underlying process is Poissonian and that its stationary solution is determined by an effective transition matrix whose leading eigenvector is easy to calculate. We conduct numerical simulations and also derive analytical results for the stationary solution under the assumption that all edges have the same waiting-time distribution. We discuss the implications of our work for dynamical processes on temporal networks and for the construction of network diagnostics that take into account their nontrivial stochastic nature.

International and Domestic Business Cycles as Dynamics of a Network of Networks

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Ikeda, Yuichi; Iyetomi, Hiroshi; Aoyama, Hideaki; Yoshikawa, Hiroshi

2014-03-01

Synchronization in business cycles has attracted economists and physicists as self-organization in the time domain. From a different point of view, international and domestic business cycles are also interesting as dynamics of a network of networks or a multi-level network. In this paper, we analyze the Indices of Industrial Production monthly time-series in Japan from January 1988 to December 2007 to develop a deeper understanding of domestic business cycles. The frequency entrainment and the partial phase locking were observed for the 16 sectors to be direct evidence of synchronization. We also showed that the information of the economic shock is carried by the phase time-series. The common shock and individual shocks are separated using phase time-series. The former dominates the economic recession in all of 1992, 1998 and 2001. In addition to the above analysis, we analyze the quarterly GDP time series for Australia, Canada, France, Italy, the United Kingdom, and the United States from Q2 1960 to Q1 2010 in order to clarify its origin. We find frequency entrainment and partial phase locking. Furthermore, a coupled limit-cycle oscillator model is developed to explain the mechanism of synchronization. In this model, the interaction due to international trade is interpreted as the origin of the synchronization. The obtained results suggest that the business cycle may be described as a dynamics of the multi-level coupled oscillators exposed to random individual shocks.

Signs over time: Statistical and visual analysis of a longitudinal signed network

NARCIS (Netherlands)

de Nooy, W.

2008-01-01

This paper presents the design and results of a statistical and visual analysis of a dynamic signed network. In addition to prevalent approaches to longitudinal networks, which analyze series of cross-sectional data, this paper focuses on network data measured in continuous time in order to explain

Individual heterogeneity generating explosive system network dynamics.

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Manrique, Pedro D; Johnson, Neil F

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Individual heterogeneity generating explosive system network dynamics

Science.gov (United States)

Manrique, Pedro D.; Johnson, Neil F.

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

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Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

2016-01-01

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

Science.gov (United States)

Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

2016-04-13

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear

Sync in Complex Dynamical Networks: Stability, Evolution, Control, and Application

OpenAIRE

Li, Xiang

2005-01-01

In the past few years, the discoveries of small-world and scale-free properties of many natural and artificial complex networks have stimulated significant advances in better understanding the relationship between the topology and the collective dynamics of complex networks. This paper reports recent progresses in the literature of synchronization of complex dynamical networks including stability criteria, network synchronizability and uniform synchronous criticality in different topologies, ...

Information dynamics of brain–heart physiological networks during sleep

International Nuclear Information System (INIS)

Faes, L; Nollo, G; Jurysta, F; Marinazzo, D

2014-01-01

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain–heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain–brain and brain–heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep. (paper)

Information dynamics of brain-heart physiological networks during sleep

Science.gov (United States)

Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

2014-10-01

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

Dynamical Networks Characterization of Geomagnetic Substorms and Transient Response to the Solar Wind State.

Science.gov (United States)

Chapman, S. C.; Dods, J.; Gjerloev, J. W.

2017-12-01

Observations of how the solar wind interacts with earth's magnetosphere, and its dynamical response, are increasingly becoming a data analytics challenge. Constellations of satellites observe the solar corona, the upstream solar wind and throughout earth's magnetosphere. These data are multipoint in space and extended in time, so in principle are ideal for study using dynamical networks to characterize the full time evolving spatial pattern. We focus here on analysis of data from the full set of 100+ auroral ground based magnetometer stations that have been collated by SuperMAG. Spatio-temporal patterns of correlation between the magnetometer time series can be used to form a dynamical network [1]. The properties of the network can then be captured by (time dependent) network parameters. This offers the possibility of characterizing detailed spatio-temporal pattern by a few parameters, so that many events can then be compared [2] with each other. Whilst networks are in widespread use in the data analytics of societal and commercial data, there are additional challenges in their application to physical timeseries. Determining whether two nodes (here, ground based magnetometer stations) are connected in a network (seeing the same dynamics) requires normalization w.r.t. the detailed sensitivities and dynamical responses of specific observing stations and seasonal conductivity variations and we have developed methods to achieve this dynamical normalization. The detailed properties of the network capture time dependent spatial correlation in the magnetometer responses and we will show how this can be used to infer a transient current system response to magnetospheric activity. [l] Dods et al, J. Geophys. Res 120, doi:10.1002/2015JA02 (2015). [2] Dods et al, J. Geophys. Res. 122, doi:10.1002/2016JA02 (2017).

Analysis of the airport network of India as a complex weighted network

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Bagler, Ganesh

2008-05-01

Transportation infrastructure of a country is one of the most important indicators of its economic growth. Here we study the Airport Network of India (ANI) which represents India’s domestic civil aviation infrastructure as a complex network. We find that ANI, a network of domestic airports connected by air links, is a small-world network characterized by a truncated power-law degree distribution and has a signature of hierarchy. We investigate ANI as a weighted network to explore its various properties and compare them with their topological counterparts. The traffic in ANI, as in the World-wide Airport Network (WAN), is found to be accumulated on interconnected groups of airports and is concentrated between large airports. In contrast to WAN, ANI is found to be having disassortative mixing which is offset by the traffic dynamics. The analysis indicates possible mechanism of formation of a national transportation network, which is different from that on a global scale.

Robustness of pinning a general complex dynamical network

International Nuclear Information System (INIS)

Wang Lei; Sun Youxian

2010-01-01

This Letter studies the robustness problem of pinning a general complex dynamical network toward an assigned synchronous evolution. Several synchronization criteria are presented to guarantee the convergence of the pinning process locally and globally by construction of Lyapunov functions. In particular, if a pinning strategy has been designed for synchronization of a given complex dynamical network, then no matter what uncertainties occur among the pinned nodes, synchronization can still be guaranteed through the pinning. The analytical results show that pinning control has a certain robustness against perturbations on network architecture: adding, deleting and changing the weights of edges. Numerical simulations illustrated by scale-free complex networks verify the theoretical results above-acquired.

Investigating the relationship between subjective drug craving and temporal dynamics of the default mode network, executive control network, and salience network in methamphetamine dependents using rsfMRI

Science.gov (United States)

Soltanian-Zadeh, Somayyeh; Hossein-Zadeh, Gholam-Ali; Shahbabaie, Alireza; Ekhtiari, Hamed

2016-03-01

Resting state functional connectivity (rsFC) studies using fMRI provides a great deal of knowledge on the spatiotemporal organization of the brain. The relationships between and within a number of resting state functional networks, namely the default mode network (DMN), salience network (SN) and executive control network (ECN) have been intensely studied in basic and clinical cognitive neuroscience [1]. However, the presumption of spatial and temporal stationarity has mostly restricted the assessment of rsFC [1]. In this study, sliding window correlation analysis and k-means clustering were exploited to examine the temporal dynamics of rsFC of these three networks in 24 abstinent methamphetamine dependents. Afterwards, using canonical correlation analysis (CCA) the possible relationship between the level of self-reported craving and the temporal dynamics was examined. Results indicate that the rsFC transits between 6 discrete "FC states" in the meth dependents. CCA results show that higher levels of craving are associated with higher probability of transiting from state 4 to 6 (positive FC of DMN-ECN getting weak and negative FC of DMN-SN appearing) and staying in state 4 (positive FC of DMN-ECN), lower probability of staying in state 2 (negative FC of DMN-ECN), transiting from state 4 to 2 (change of positive FC of DMN-ECN to negative FC), and transiting from state 3 to 5 (appearance of negative FC of DMN-SN and positive FC of DMN-ECN with the presence of negative FC of SN-ECN). Quantitative measures of temporal dynamics in large-scale brain networks could bring new added values to increase potentials for applications of rsfMRI in addiction medicine.

Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

Directory of Open Access Journals (Sweden)

Axel Hutt

Full Text Available Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

Science.gov (United States)

Hutt, Axel; Mierau, Andreas; Lefebvre, Jérémie

Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

Dynamic and modular gene regulatory networks drive the development of gametogenesis.

Science.gov (United States)

Che, Dongxue; Wang, Yang; Bai, Weiyang; Li, Leijie; Liu, Guiyou; Zhang, Liangcai; Zuo, Yongchun; Tao, Shiheng; Hua, Jinlian; Liao, Mingzhi

2017-07-01

Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Agent-based modeling and network dynamics

CERN Document Server

Namatame, Akira

2016-01-01

The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

Comparison of neural network applications for channel assignment in cellular TDMA networks and dynamically sectored PCS networks

Science.gov (United States)

Hortos, William S.

1997-04-01

The use of artificial neural networks (NNs) to address the channel assignment problem (CAP) for cellular time-division multiple access and code-division multiple access networks has previously been investigated by this author and many others. The investigations to date have been based on a hexagonal cell structure established by omnidirectional antennas at the base stations. No account was taken of the use of spatial isolation enabled by directional antennas to reduce interference between mobiles. Any reduction in interference translates into increased capacity and consequently alters the performance of the NNs. Previous studies have sought to improve the performance of Hopfield- Tank network algorithms and self-organizing feature map algorithms applied primarily to static channel assignment (SCA) for cellular networks that handle uniformly distributed, stationary traffic in each cell for a single type of service. The resulting algorithms minimize energy functions representing interference constraint and ad hoc conditions that promote convergence to optimal solutions. While the structures of the derived neural network algorithms (NNAs) offer the potential advantages of inherent parallelism and adaptability to changing system conditions, this potential has yet to be fulfilled the CAP for emerging mobile networks. The next-generation communication infrastructures must accommodate dynamic operating conditions. Macrocell topologies are being refined to microcells and picocells that can be dynamically sectored by adaptively controlled, directional antennas and programmable transceivers. These networks must support the time-varying demands for personal communication services (PCS) that simultaneously carry voice, data and video and, thus, require new dynamic channel assignment (DCA) algorithms. This paper examines the impact of dynamic cell sectoring and geometric conditioning on NNAs developed for SCA in omnicell networks with stationary traffic to improve the metrics

The architecture of dynamic reservoir in the echo state network

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Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

Hydration dynamics of a lipid membrane: Hydrogen bond networks and lipid-lipid associations

Science.gov (United States)

Srivastava, Abhinav; Debnath, Ananya

2018-03-01

Dynamics of hydration layers of a dimyristoylphosphatidylcholine (DMPC) bilayer are investigated using an all atom molecular dynamics simulation. Based upon the geometric criteria, continuously residing interface water molecules which form hydrogen bonds solely among themselves and then concertedly hydrogen bonded to carbonyl, phosphate, and glycerol head groups of DMPC are identified. The interface water hydrogen bonded to lipids shows slower relaxation rates for translational and rotational dynamics compared to that of the bulk water and is found to follow sub-diffusive and non-diffusive behaviors, respectively. The mean square displacements and the reorientational auto-correlation functions are slowest for the interfacial waters hydrogen bonded to the carbonyl oxygen since these are buried deep in the hydrophobic core among all interfacial water studied. The intermittent hydrogen bond auto-correlation functions are calculated, which allows breaking and reformations of the hydrogen bonds. The auto-correlation functions for interfacial hydrogen bonded networks develop humps during a transition from cage-like motion to eventual power law behavior of t-3/2. The asymptotic t-3/2 behavior indicates translational diffusion dictated dynamics during hydrogen bond breaking and formation irrespective of the nature of the chemical confinement. Employing reactive flux correlation analysis, the forward rate constant of hydrogen bond breaking and formation is calculated which is used to obtain Gibbs energy of activation of the hydrogen bond breaking. The relaxation rates of the networks buried in the hydrophobic core are slower than the networks near the lipid-water interface which is again slower than bulk due to the higher Gibbs energy of activation. Since hydrogen bond breakage follows a translational diffusion dictated mechanism, chemically confined hydrogen bond networks need an activation energy to diffuse through water depleted hydrophobic environments. Our calculations

Congested Link Inference Algorithms in Dynamic Routing IP Network

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Yu Chen

2017-01-01

Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.

Dynamic analysis of stochastic bidirectional associative memory neural networks with delays

International Nuclear Information System (INIS)

Zhao Hongyong; Ding Nan

2007-01-01

In this paper, stochastic bidirectional associative memory neural networks model with delays is considered. By constructing Lyapunov functionals, and using stochastic analysis method and inequality technique, we give some sufficient criteria ensuring almost sure exponential stability, pth exponential stability and mean value exponential stability. The obtained criteria can be used as theoretic guidance to stabilize neural networks in practical applications when stochastic noise is taken into consideration

Network Dynamics of Innovation Processes

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Iacopini, Iacopo; Milojević, Staša; Latora, Vito

2018-01-01

We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the "adjacent possible," and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

Science.gov (United States)

Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Network Analysis Approach to Stroke Care and Assistance Provision: An Empirical Study

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Szczygiel Nina

2017-06-01

Full Text Available To model and analyse stroke care and assistance provision in the Portuguese context from the network perspective. We used the network theory as a theoretical foundation for the study. The model proposed by Frey et al. (2006 was used to elicit and comprehend possible interactions and relations between organisations expected to be involved in the provision of care and assistance to stroke patients in their pathway to rehabilitation. Providers were identified and contacted to evaluate the nature and intensity of relationships. Network analysis was performed with the NodeXL software package. Analysis of 509 entities based on about 260 000 entries indicates that stroke care provision in the evaluated context is best captured in the coalition-collaboration setting, which appears to best demonstrate the character of the network. Information from analysis of the collaboration stage was not sufficient to determine the network dynamics. Application of the network theory to understand interorganisational dynamics of the complex health care context. Empirical validation of the model proposed by Frey et al. (2006 in terms of its operationalisation and the way it actually reflects the practical context. Examination and analysis of interorganisational relationships and its contribution to management of compound health care context involving actors from various sectors.

Evolving dynamics of trading behavior based on coordination game in complex networks

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Bian, Yue-tang; Xu, Lu; Li, Jin-sheng

2016-05-01

This work concerns the modeling of evolvement of trading behavior in stock markets. Based on the assumption of the investors' limited rationality, the evolution mechanism of trading behavior is modeled according to the investment strategy of coordination game in network, that investors are prone to imitate their neighbors' activity through comprehensive analysis on the risk dominance degree of certain investment behavior, the network topology of their relationship and its heterogeneity. We investigate by mean-field analysis and extensive simulations the evolution of investors' trading behavior in various typical networks under different risk dominance degree of investment behavior. Our results indicate that the evolution of investors' behavior is affected by the network structure of stock market and the effect of risk dominance degree of investment behavior; the stability of equilibrium states of investors' behavior dynamics is directly related with the risk dominance degree of some behavior; connectivity and heterogeneity of the network plays an important role in the evolution of the investment behavior in stock market.

OTDM Networking for Short Range High-Capacity Highly Dynamic Networks

DEFF Research Database (Denmark)

Medhin, Ashenafi Kiros

This PhD thesis aims at investigating the possibility of designing energy-efficient high-capacity (up to Tbit/s) optical network scenarios, leveraging on the effect of collective switching of many bits simultaneously, as is inherent in high bit rate serial optical data signals. The focus...... is on short range highly dynamic networks, catering to data center needs. The investigation concerns optical network scenarios, and experimental implementations of high bit rate serial data packet generation and reception, scalable optical packet labeling, simple optical label extraction and stable ultra...

Infraslow Electroencephalographic and Dynamic Resting State Network Activity.

Science.gov (United States)

Grooms, Joshua K; Thompson, Garth J; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H; Epstein, Charles M; Keilholz, Shella D

2017-06-01

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Changes in dynamic resting state network connectivity following aphasia therapy.

Science.gov (United States)

Duncan, E Susan; Small, Steven L

2017-10-24

Resting state magnetic resonance imaging (rsfMRI) permits observation of intrinsic neural networks produced by task-independent correlations in low frequency brain activity. Various resting state networks have been described, with each thought to reflect common engagement in some shared function. There has been limited investigation of the plasticity in these network relationships after stroke or induced by therapy. Twelve individuals with language disorders after stroke (aphasia) were imaged at multiple time points before (baseline) and after an imitation-based aphasia therapy. Language assessment using a narrative production task was performed at the same time points. Group independent component analysis (ICA) was performed on the rsfMRI data to identify resting state networks. A sliding window approach was then applied to assess the dynamic nature of the correlations among these networks. Network correlations during each 30-second window were used to cluster the data into ten states for each window at each time point for each subject. Correlation was performed between changes in time spent in each state and therapeutic gains on the narrative task. The amount of time spent in a single one of the (ten overall) dynamic states was positively associated with behavioral improvement on the narrative task at the 6-week post-therapy maintenance interval, when compared with either baseline or assessment immediately following therapy. This particular state was characterized by minimal correlation among the task-independent resting state networks. Increased functional independence and segregation of resting state networks underlies improvement on a narrative production task following imitation-based aphasia treatment. This has important clinical implications for the targeting of noninvasive brain stimulation in post-stroke remediation.

Competitive Dynamics on Complex Networks

Science.gov (United States)

Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

2014-07-01

We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

Active influence in dynamical models of structural balance in social networks

Science.gov (United States)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Role of graph architecture in controlling dynamical networks with applications to neural systems

Science.gov (United States)

Kim, Jason Z.; Soffer, Jonathan M.; Kahn, Ari E.; Vettel, Jean M.; Pasqualetti, Fabio; Bassett, Danielle S.

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

perception of communication network fraud dynamics by network ...

African Journals Online (AJOL)

ES Obe

work fraud dynamics by network administrators and stakeholders. In considering ... cyber crime within the last two years. How- ever, two-thirds of the ... ˆ increased exposure to unpredictable fi- nancial losses ... The intentions of the customers are reflected ..... 'There is a 95% confidence that the differ- ence between the ...

Visually Augmented Analysis of Socio-Technical Networks in Engineering Systems Design Research

DEFF Research Database (Denmark)

Storga, M.; Stankovic, T.; Cash, Philip

2013-01-01

In characterizing systems behaviour, complex-systems scientists use tools from a variety of disciplines, including nonlinear dynamics, information theory, computation theory, evolutionary biology and social network analysis, among others. All of these topics have been studied for some time......, but only fairly recently has the study of networks in general become a major topic of research in complex engineering systems. The research reported in this paper is discussing how the visually augmented analysis of complex socio-networks (networks of people and technology engaged in a product...

Complex networks: when random walk dynamics equals synchronization

International Nuclear Information System (INIS)

Kriener, Birgit; Anand, Lishma; Timme, Marc

2012-01-01

Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits. (paper)

Performance Analysis of Control Signal Transmission Technique for Cognitive Radios in Dynamic Spectrum Access Networks

Science.gov (United States)

Sakata, Ren; Tomioka, Tazuko; Kobayashi, Takahiro

When cognitive radio (CR) systems dynamically use the frequency band, a control signal is necessary to indicate which carrier frequencies are currently available in the network. In order to keep efficient spectrum utilization, this control signal also should be transmitted based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers have to receive control signals without knowledge of their carrier frequencies. To enable such transmission and reception, this paper proposes a novel scheme called DCPT (Differential Code Parallel Transmission). With DCPT, receivers can receive low-rate information with no knowledge of the carrier frequencies. The transmitter transmits two signals whose carrier frequencies are spaced by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver acquires the DCPT signal, it multiplies the signal by a frequency-shifted version of the signal; this yields a DC component that represents the data signal which is then demodulated. The performance was evaluated by means of numerical analysis and computer simulation. We confirmed that DCPT operates successfully even under severe interference if its parameters are appropriately configured.

Dynamical systems on networks a tutorial

CERN Document Server

Porter, Mason A

2016-01-01

This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Appli...

SNAP: A General Purpose Network Analysis and Graph Mining Library.

Science.gov (United States)

Leskovec, Jure; Sosič, Rok

2016-10-01

Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and meta-data on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic, they can be modified during the computation at low cost. SNAP is provided as an open source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.

The cell wall of Arabidopsis thaliana influences actin network dynamics.

Science.gov (United States)

Tolmie, Frances; Poulet, Axel; McKenna, Joseph; Sassmann, Stefan; Graumann, Katja; Deeks, Michael; Runions, John

2017-07-20

In plant cells, molecular connections link the cell wall-plasma membrane-actin cytoskeleton to form a continuum. It is hypothesized that the cell wall provides stable anchor points around which the actin cytoskeleton remodels. Here we use live cell imaging of fluorescently labelled marker proteins to quantify the organization and dynamics of the actin cytoskeleton and to determine the impact of disrupting connections within the continuum. Labelling of the actin cytoskeleton with green fluorescent protein (GFP)-fimbrin actin-binding domain 2 (FABD2) resulted in a network composed of fine filaments and thicker bundles that appeared as a highly dynamic remodelling meshwork. This differed substantially from the GFP-Lifeact-labelled network that appeared much more sparse with thick bundles that underwent 'simple movement', in which the bundles slightly change position, but in such a manner that the structure of the network was not substantially altered during the time of observation. Label-dependent differences in actin network morphology and remodelling necessitated development of two new image analysis techniques. The first of these, 'pairwise image subtraction', was applied to measurement of the more rapidly remodelling actin network labelled with GFP-FABD2, while the second, 'cumulative fluorescence intensity', was used to measure bulk remodelling of the actin cytoskeleton when labelled with GFP-Lifeact. In each case, these analysis techniques show that the actin cytoskeleton has a decreased rate of bulk remodelling when the cell wall-plasma membrane-actin continuum is disrupted either by plasmolysis or with isoxaben, a drug that specifically inhibits cellulose deposition. Changes in the rate of actin remodelling also affect its functionality, as observed by alteration in Golgi body motility. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Correlation dimension based nonlinear analysis of network traffics with different application protocols

International Nuclear Information System (INIS)

Wang Jun-Song; Yuan Jing; Li Qiang; Yuan Rui-Xi

2011-01-01

This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols—HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components. (general)

Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index

Directory of Open Access Journals (Sweden)

Ming Li

2018-06-01

Full Text Available The effective prediction of storm track (ST is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII, a new probabilistic prediction model was proposed based on dynamic Bayesian network (DBN and wavelet analysis (WA. We introduced probability theory and graph theory for the first time to quantitatively describe the nonlinear relationship and uncertain interaction of the ST system. Then a casual prediction network (i.e., DBN was constructed through wavelet decomposition, structural learning, parameter learning, and probabilistic inference, which was used for expression of relation among predictors and probabilistic prediction of STII. The intensity prediction of the North Pacific ST with data from 1961–2010 showed that the new model was able to give more comprehensive prediction information and higher prediction accuracy and had strong generalization ability and good stability.

Rumor Diffusion in an Interests-Based Dynamic Social Network

Directory of Open Access Journals (Sweden)

Mingsheng Tang

2013-01-01

Full Text Available To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1 positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2 with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3 a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4 a network with a smaller clustering coefficient has a larger efficiency.

Simulating market dynamics: interactions between consumer psychology and social networks.

Science.gov (United States)

Janssen, Marco A; Jager, Wander

2003-01-01

Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

Dynamic interactions of the cortical networks during thought suppression.

Science.gov (United States)

Aso, Toshihiko; Nishimura, Kazuo; Kiyonaka, Takashi; Aoki, Takaaki; Inagawa, Michiyo; Matsuhashi, Masao; Tobinaga, Yoshikazu; Fukuyama, Hidenao

2016-08-01

Thought suppression has spurred extensive research in clinical and preclinical fields, particularly with regard to the paradoxical aspects of this behavior. However, the involvement of the brain's inhibitory system in the dynamics underlying the continuous effort to suppress thoughts has yet to be clarified. This study aims to provide a unified perspective for the volitional suppression of internal events incorporating the current understanding of the brain's inhibitory system. Twenty healthy volunteers underwent functional magnetic resonance imaging while they performed thought suppression blocks alternating with visual imagery blocks. The whole dataset was decomposed by group-independent component analysis into 30 components. After discarding noise components, the 20 valid components were subjected to further analysis of their temporal properties including task-relatedness and between-component residual correlation. Combining a long task period and a data-driven approach, we observed a right-side-dominant, lateral frontoparietal network to be strongly suppression related. This network exhibited increased fluctuation during suppression, which is compatible with the well-known difficulty of suppression maintenance. Between-network correlation provided further insight into the coordinated engagement of the executive control and dorsal attention networks, as well as the reciprocal activation of imagery-related components, thus revealing neural substrates associated with the rivalry between intrusive thoughts and the suppression process.

State-dependent intrinsic predictability of cortical network dynamics.

Directory of Open Access Journals (Sweden)

Leila Fakhraei

Full Text Available The information encoded in cortical circuit dynamics is fleeting, changing from moment to moment as new input arrives and ongoing intracortical interactions progress. A combination of deterministic and stochastic biophysical mechanisms governs how cortical dynamics at one moment evolve from cortical dynamics in recently preceding moments. Such temporal continuity of cortical dynamics is fundamental to many aspects of cortex function but is not well understood. Here we study temporal continuity by attempting to predict cortical population dynamics (multisite local field potential based on its own recent history in somatosensory cortex of anesthetized rats and in a computational network-level model. We found that the intrinsic predictability of cortical dynamics was dependent on multiple factors including cortical state, synaptic inhibition, and how far into the future the prediction extends. By pharmacologically tuning synaptic inhibition, we obtained a continuum of cortical states with asynchronous population activity at one extreme and stronger, spatially extended synchrony at the other extreme. Intermediate between these extremes we observed evidence for a special regime of population dynamics called criticality. Predictability of the near future (10-100 ms increased as the cortical state was tuned from asynchronous to synchronous. Predictability of the more distant future (>1 s was generally poor, but, surprisingly, was higher for asynchronous states compared to synchronous states. These experimental results were confirmed in a computational network model of spiking excitatory and inhibitory neurons. Our findings demonstrate that determinism and predictability of network dynamics depend on cortical state and the time-scale of the dynamics.

Singular Perturbation Analysis and Gene Regulatory Networks with Delay

Science.gov (United States)

Shlykova, Irina; Ponosov, Arcady

2009-09-01

There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

Dynamic Mobile IP routers in ad hoc networks

NARCIS (Netherlands)

Kock, B.A.; Schmidt, J.R.

2005-01-01

This paper describes a concept combining mobile IP and ad hoc routing to create a robust mobile network. In this network all nodes are mobile and globally and locally reachable under the same IP address. Essential for implementing this network are the dynamic mobile IP routers. They act as gateways

Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

International Nuclear Information System (INIS)

Rong Bao; Rui Xiaoting; Tao Ling

2012-01-01

In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.

Complex and unexpected dynamics in simple genetic regulatory networks

Science.gov (United States)

Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

2014-03-01

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

A Network-Based Data Envelope Analysis Model in a Dynamic Balanced Score Card

Directory of Open Access Journals (Sweden)

Mojtaba Akbarian

2015-01-01

Full Text Available Performance assessment during the time and along with strategies is the most important requirements of top managers. To assess the performance, a balanced score card (BSC along with strategic goals and a data envelopment analysis (DEA are used as powerful qualitative and quantitative tools, respectively. By integrating these two models, their strengths are used and their weaknesses are removed. In this paper, an integrated framework of the BSC and DEA models is proposed for measuring the efficiency during the time and along with strategies based on the time delay of the lag key performance indicators (KPIs of the BSC model. The causal relationships during the time among perspectives of the BSC model are drawn as dynamic BSC at first. Then, after identifying the network-DEA structure, a new objective function for measuring the efficiency of nine subsidiary refineries of the National Iranian Oil Refining and Distribution Company (NIORDC during the time and along with strategies is developed.

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

Directory of Open Access Journals (Sweden)

E. Damaraju

2014-01-01

Full Text Available Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length, and a dynamic sense, computed using sliding windows (44 s in length and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual, as well as reduced connectivity (hypoconnectivity between sensory networks from all modalities. Dynamic analysis suggests that (1, on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2, that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity

Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

OpenAIRE

Xerxes D. Arsiwalla; Riccardo eZucca; Alberto eBetella; Enrique eMartinez; David eDalmazzo; Pedro eOmedas; Gustavo eDeco; Gustavo eDeco; Paul F.M.J. Verschure; Paul F.M.J. Verschure

2015-01-01

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

OpenAIRE

Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martínez, Enrique, 1961-; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

2015-01-01

BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

The geometry of chaotic dynamics — a complex network perspective

Science.gov (United States)

Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

2011-12-01

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

The consideration of dynamics and control in the design of heat exchanger networks

International Nuclear Information System (INIS)

Reimann, K.A.

1986-03-01

The heat exchanger network method is a way of abstracting the enthalpy and heat flows from the blueprints of a planned or existing processing plant. It enables a systematic design of a plant-wide heat recovery system which is optimal with regard to energy costs, capital costs and operational requirements. A heat exchanger network is a representation of all heat transfer relations between hot process streams and cold process streams within a plant. During the past ten years, the optimal design of heat exchanger networks (i.e. the optimal arrangement of heat transfer relations within a plant) has developed into a field of research of its own. Both, static methods ('interaction analysis') and dynamic methods ('process reaction curve analysis') from control theory have been used to explore the new field of heat exchanger network dynamics. As a major tool, an interactive, portable computer program for network simulation and controllability assessment has been developed (it is available as a design tool within the frame of the International Energy Agency). Based on the well-understood global parameters: effectiveness and NTU, which follow from the network design, some straightforward methods covering the following topics are presented: - 'paths' for control and disturbance signal transfer across the network, - locations of control bypasses around heat exchangers, and their capacity of emitting control signals or absorbing disturbances, - influence of the equipment besides the heat exchangers (which can be regarded as 'surrounding' the network, thus forming an 'associated' network). It has been found that networks which are designed according to the 'pinch-based' method have a potential for good controllability. It is shown how, using the freedoms given in the 'pinch-based' design and the above-mentioned methods, that potential is put into effect. (author)

Mathematical model for spreading dynamics of social network worms

International Nuclear Information System (INIS)

Sun, Xin; Liu, Yan-Heng; Han, Jia-Wei; Liu, Xue-Jie; Li, Bin; Li, Jin

2012-01-01

In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks

Cell fate reprogramming by control of intracellular network dynamics

Science.gov (United States)

Zanudo, Jorge G. T.; Albert, Reka

Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.

Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

Science.gov (United States)

Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

2016-11-01

Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

Complexity and network dynamics in physiological adaptation: An integrated view

OpenAIRE

Baffy, Gyorgy; Loscalzo, Joseph

2014-01-01

Living organisms constantly interact with their surroundings and sustain internal stability against perturbations. This dynamic process follows three fundamental strategies (restore, explore, and abandon) articulated in historical concepts of physiological adaptation such as homeostasis, allostasis, and the general adaptation syndrome. These strategies correspond to elementary forms of behavior (ordered, chaotic, and static) in complex adaptive systems and invite a network-based analysis of t...

Dynamics of epidemic diseases on a growing adaptive network.

Science.gov (United States)

Demirel, Güven; Barter, Edmund; Gross, Thilo

2017-02-10

The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.

Social networks dynamics revealed by temporal analysis: An example in a non-human primate (Macaca sylvanus) in "La Forêt des Singes".

Science.gov (United States)

Sosa, Sebastian; Zhang, Peng; Cabanes, Guénaël

2017-06-01

This study applied a temporal social network analysis model to describe three affiliative social networks (allogrooming, sleep in contact, and triadic interaction) in a non-human primate species, Macaca sylvanus. Three main social mechanisms were examined to determine interactional patterns among group members, namely preferential attachment (i.e., highly connected individuals are more likely to form new connections), triadic closure (new connections occur via previous close connections), and homophily (individuals interact preferably with others with similar attributes). Preferential attachment was only observed for triadic interaction network. Triadic closure was significant in allogrooming and triadic interaction networks. Finally, gender homophily was seasonal for allogrooming and sleep in contact networks, and observed in each period for triadic interaction network. These individual-based behaviors are based on individual reactions, and their analysis can shed light on the formation of the affiliative networks determining ultimate coalition networks, and how these networks may evolve over time. A focus on individual behaviors is necessary for a global interactional approach to understanding social behavior rules and strategies. When combined, these social processes could make animal social networks more resilient, thus enabling them to face drastic environmental changes. This is the first study to pinpoint some of the processes underlying the formation of a social structure in a non-human primate species, and identify common mechanisms with humans. The approach used in this study provides an ideal tool for further research seeking to answer long-standing questions about social network dynamics. © 2017 Wiley Periodicals, Inc.

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)

Dynamic hydro-climatic networks in pristine and regulated rivers

Science.gov (United States)

Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

2014-12-01

Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes

Impact of constrained rewiring on network structure and node dynamics

Science.gov (United States)

Rattana, P.; Berthouze, L.; Kiss, I. Z.

2014-11-01

In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.

Parameter estimation of breast tumour using dynamic neural network from thermal pattern

Directory of Open Access Journals (Sweden)

Elham Saniei

2016-11-01

Full Text Available This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN. Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients were promising in terms of the model’s potential for retrieving tumour parameters.

Context-dependent retrieval of information by neural-network dynamics with continuous attractors.

Science.gov (United States)

Tsuboshita, Yukihiro; Okamoto, Hiroshi

2007-08-01

Memory retrieval in neural networks has traditionally been described by dynamic systems with discrete attractors. However, recent neurophysiological findings of graded persistent activity suggest that memory retrieval in the brain is more likely to be described by dynamic systems with continuous attractors. To explore what sort of information processing is achieved by continuous-attractor dynamics, keyword extraction from documents by a network of bistable neurons, which gives robust continuous attractors, is examined. Given an associative network of terms, a continuous attractor led by propagation of neuronal activation in this network appears to represent keywords that express underlying meaning of a document encoded in the initial state of the network-activation pattern. A dominant hypothesis in cognitive psychology is that long-term memory is archived in the network structure, which resembles associative networks of terms. Our results suggest that keyword extraction by the neural-network dynamics with continuous attractors might symbolically represent context-dependent retrieval of short-term memory from long-term memory in the brain.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

Science.gov (United States)

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Fragility in dynamic networks: application to neural networks in the epileptic cortex.

Science.gov (United States)

Sritharan, Duluxan; Sarma, Sridevi V

2014-10-01

Epilepsy is a network phenomenon characterized by atypical activity at the neuronal and population levels during seizures, including tonic spiking, increased heterogeneity in spiking rates, and synchronization. The etiology of epilepsy is unclear, but a common theme among proposed mechanisms is that structural connectivity between neurons is altered. It is hypothesized that epilepsy arises not from random changes in connectivity, but from specific structural changes to the most fragile nodes or neurons in the network. In this letter, the minimum energy perturbation on functional connectivity required to destabilize linear networks is derived. Perturbation results are then applied to a probabilistic nonlinear neural network model that operates at a stable fixed point. That is, if a small stimulus is applied to the network, the activation probabilities of each neuron respond transiently but eventually recover to their baseline values. When the perturbed network is destabilized, the activation probabilities shift to larger or smaller values or oscillate when a small stimulus is applied. Finally, the structural modifications to the neural network that achieve the functional perturbation are derived. Simulations of the unperturbed and perturbed networks qualitatively reflect neuronal activity observed in epilepsy patients, suggesting that the changes in network dynamics due to destabilizing perturbations, including the emergence of an unstable manifold or a stable limit cycle, may be indicative of neuronal or population dynamics during seizure. That is, the epileptic cortex is always on the brink of instability and minute changes in the synaptic weights associated with the most fragile node can suddenly destabilize the network to cause seizures. Finally, the theory developed here and its interpretation of epileptic networks enables the design of a straightforward feedback controller that first detects when the network has destabilized and then applies linear state

Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

International Nuclear Information System (INIS)

Khakzad, Nima

2015-01-01

A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant. - Highlights: • A Dynamic Bayesian Network methodology has been developed to model domino effects. • Considering time-dependencies, both spatial and temporal evolutions of domino effects have been modeled. • The concept of most probable sequence of accidents has been proposed instead of the most probable combination of accidents. • Using backward analysis, the most vulnerable units have been identified during a potential domino effect. • The proposed methodology does not need to identify a unique primary unit (accident) for domino effect modeling

Energy-Aware Routing Optimization in Dynamic GMPLS Controlled Optical Networks

DEFF Research Database (Denmark)

Wang, Jiayuan; Ricciardi, Sergio; Fagertun, Anna Manolova

2012-01-01

In this paper, routing optimizations based on energy sources are proposed in dynamic GMPLS controlled optical networks. The influences of re-routing and load balancing factors on the algorithm are evaluated, with a focus on different re-routing thresholds. Results from dynamic network simulations...

Predictive coding of dynamical variables in balanced spiking networks.

Science.gov (United States)

Boerlin, Martin; Machens, Christian K; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

Dynamics of nephron-vascular network

DEFF Research Database (Denmark)

Postnov, Dmitry; Postnov, D E; Marsh, D J

2012-01-01

The paper presents a modeling study of the spatial dynamics of a nephro-vascular network consisting of individual nephrons connected via a tree-like vascular branching structure. We focus on the effects of nonlinear mechanisms that are responsible for the formation of synchronous patterns in order...

Nonlinear identification of process dynamics using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.F.; Chong, K.T.

1992-01-01

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

Tissue factor activates allosteric networks in factor VIIa through structural and dynamic changes

DEFF Research Database (Denmark)

Madsen, Jesper Jonasson; Persson, E.; Olsen, O. H.

2015-01-01

that are not likely to be inferred from mutagenesis studies. Furthermore, paths from Met306 to Ile153 (N-terminus) and Trp364, both representing hallmark residues of allostery, are 7% and 37% longer, respectively, in free FVIIa. Thus, there is significantly weaker coupling between the TF contact point and key......Background: Tissue factor (TF) promotes colocalization of enzyme (factorVIIa) and substrate (FX or FIX), and stabilizes the active conformation of FVIIa. Details on how TF induces structural and dynamic changes in the catalytic domain of FVIIa to enhance its efficiency remain elusive. Objective......: To elucidate the activation of allosteric networks in the catalytic domain of the FVIIa protease it is when bound to TF.MethodsLong-timescale molecular dynamics simulations of FVIIa, free and in complex with TF, were executed and analyzed by dynamic network analysis. Results: Allosteric paths of correlated...

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

Directory of Open Access Journals (Sweden)

Evan S Schaffer

2013-10-01

Full Text Available Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

Science.gov (United States)

Schaffer, Evan S; Ostojic, Srdjan; Abbott, L F

2013-10-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

Directory of Open Access Journals (Sweden)

Wei Kong

2014-01-01

Full Text Available Alzheimer’s disease (AD is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA, which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

Synchronization of complex delayed dynamical networks with nonlinearly coupled nodes

International Nuclear Information System (INIS)

Liu Tao; Zhao Jun; Hill, David J.

2009-01-01

In this paper, we study the global synchronization of nonlinearly coupled complex delayed dynamical networks with both directed and undirected graphs. Via Lyapunov-Krasovskii stability theory and the network topology, we investigate the global synchronization of such networks. Under the assumption that coupling coefficients are known, a family of delay-independent decentralized nonlinear feedback controllers are designed to globally synchronize the networks. When coupling coefficients are unavailable, an adaptive mechanism is introduced to synthesize a family of delay-independent decentralized adaptive controllers which guarantee the global synchronization of the uncertain networks. Two numerical examples of directed and undirected delayed dynamical network are given, respectively, using the Lorenz system as the nodes of the networks, which demonstrate the effectiveness of proposed results.

Dynamic baseline detection method for power data network service

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Chen, Wei

2017-08-01

This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables

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Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc

2017-01-01

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. PMID:28572780

Network Firewall Dynamics and the Subsaturation Stabilization of HIV.

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Khan, Bilal; Dombrowski, Kirk; Saad, Mohamed; McLean, Katherine; Friedman, Samuel

2013-01-01

In 2001, Friedman et al. conjectured the existence of a "firewall effect" in which individuals who are infected with HIV, but remain in a state of low infectiousness, serve to prevent the virus from spreading. To evaluate this historical conjecture, we develop a new graph-theoretic measure that quantifies the extent to which Friedman's firewall hypothesis(FH)holds in a risk network. We compute this new measure across simulated trajectories of a stochastic discrete dynamical system that models a social network of 25,000 individuals engaging in risk acts over a period of 15 years. The model's parameters are based on analyses of data collected in prior studies of the real-world risk networks of people who inject drugs (PWID) in New York City. Analysis of system trajectories reveals the structural mechanisms by which individuals with mature HIV infections tend to partition the network into homogeneous clusters (with respect to infection status) and how uninfected clusters remain relatively stable (with respect to infection status) over long stretches of time. We confirm the spontaneous emergence of network firewalls in the system and reveal their structural role in the nonspreading of HIV.

Spontaneous formation of dynamical groups in an adaptive networked system

International Nuclear Information System (INIS)

Li Menghui; Guan Shuguang; Lai, C-H

2010-01-01

In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.

Performance Analysis of On-Demand Routing Protocols in Wireless Mesh Networks

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Arafatur RAHMAN

2009-01-01

Full Text Available Wireless Mesh Networks (WMNs have recently gained a lot of popularity due to their rapid deployment and instant communication capabilities. WMNs are dynamically self-organizing, self-configuring and self-healing with the nodes in the network automatically establishing an adiej hoc network and preserving the mesh connectivity. Designing a routing protocol for WMNs requires several aspects to consider, such as wireless networks, fixed applications, mobile applications, scalability, better performance metrics, efficient routing within infrastructure, load balancing, throughput enhancement, interference, robustness etc. To support communication, various routing protocols are designed for various networks (e.g. ad hoc, sensor, wired etc.. However, all these protocols are not suitable for WMNs, because of the architectural differences among the networks. In this paper, a detailed simulation based performance study and analysis is performed on the reactive routing protocols to verify the suitability of these protocols over such kind of networks. Ad Hoc On-Demand Distance Vector (AODV, Dynamic Source Routing (DSR and Dynamic MANET On-demand (DYMO routing protocol are considered as the representative of reactive routing protocols. The performance differentials are investigated using varying traffic load and number of source. Based on the simulation results, how the performance of each protocol can be improved is also recommended.

Strategic tradeoffs in competitor dynamics on adaptive networks.

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Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric

2017-08-08

Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.

Hybrid modeling and empirical analysis of automobile supply chain network

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Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying

2017-05-01

Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

Dynamics of domain wall networks with junctions

International Nuclear Information System (INIS)

Avelino, P. P.; Oliveira, J. C. R. E.; Martins, C. J. A. P.; Menezes, J.; Menezes, R.

2008-01-01

We use a combination of analytic tools and an extensive set of the largest and most accurate three-dimensional field theory numerical simulations to study the dynamics of domain wall networks with junctions. We build upon our previous work and consider a class of models which, in the limit of large number N of coupled scalar fields, approaches the so-called ''ideal'' model (in terms of its potential to lead to network frustration). We consider values of N between N=2 and N=20, and a range of cosmological epochs, and we also compare this class of models with other toy models used in the past. In all cases we find compelling evidence for a gradual approach to scaling, strongly supporting our no-frustration conjecture. We also discuss the various possible types of junctions (including cases where there is a hierarchy of them) and their roles in the dynamics of the network. Finally, we provide a cosmological Zel'dovich-type bound on the energy scale of this kind of defect network: it must be lower than 10 keV.

Spatio-temporal organization of dynamics in a two-dimensional periodically driven vortex flow: A Lagrangian flow network perspective.

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Lindner, Michael; Donner, Reik V

2017-03-01

We study the Lagrangian dynamics of passive tracers in a simple model of a driven two-dimensional vortex resembling real-world geophysical flow patterns. Using a discrete approximation of the system's transfer operator, we construct a directed network that describes the exchange of mass between distinct regions of the flow domain. By studying different measures characterizing flow network connectivity at different time-scales, we are able to identify the location of dynamically invariant structures and regions of maximum dispersion. Specifically, our approach allows us to delimit co-existing flow regimes with different dynamics. To validate our findings, we compare several network characteristics to the well-established finite-time Lyapunov exponents and apply a receiver operating characteristic analysis to identify network measures that are particularly useful for unveiling the skeleton of Lagrangian chaos.

Unveiling Hidden Dynamics of Hippo Signalling: A Systems Analysis

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Sung-Young Shin

2016-08-01

Full Text Available The Hippo signalling pathway has recently emerged as an important regulator of cell apoptosis and proliferation with significant implications in human diseases. In mammals, the pathway contains the core kinases MST1/2, which phosphorylate and activate LATS1/2 kinases. The pro-apoptotic function of the MST/LATS signalling axis was previously linked to the Akt and ERK MAPK pathways, demonstrating that the Hippo pathway does not act alone but crosstalks with other signalling pathways to coordinate network dynamics and cellular outcomes. These crosstalks were characterised by a multitude of complex regulatory mechanisms involving competitive protein-protein interactions and phosphorylation mediated feedback loops. However, how these different mechanisms interplay in different cellular contexts to drive the context-specific network dynamics of Hippo-ERK signalling remains elusive. Using mathematical modelling and computational analysis, we uncovered that the Hippo-ERK network can generate highly diverse dynamical profiles that can be clustered into distinct dose-response patterns. For each pattern, we offered mechanistic explanation that defines when and how the observed phenomenon can arise. We demonstrated that Akt displays opposing, dose-dependent functions towards ERK, which are mediated by the balance between the Raf-1/MST2 protein interaction module and the LATS1 mediated feedback regulation. Moreover, Ras displays a multi-functional role and drives biphasic responses of both MST2 and ERK activities; which are critically governed by the competitive protein interaction between MST2 and Raf-1. Our study represents the first in-depth and systematic analysis of the Hippo-ERK network dynamics and provides a concrete foundation for future studies.

Dynamics of the cell-cycle network under genome-rewiring perturbations

International Nuclear Information System (INIS)

Katzir, Yair; Elhanati, Yuval; Braun, Erez; Averbukh, Inna

2013-01-01

The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycle's promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein–DNA and protein–protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited ‘resources’ and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes

Design of multi-phase dynamic chemical networks

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Chen, Chenrui; Tan, Junjun; Hsieh, Ming-Chien; Pan, Ting; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.

2017-08-01

Template-directed polymerization reactions enable the accurate storage and processing of nature's biopolymer information. This mutualistic relationship of nucleic acids and proteins, a network known as life's central dogma, is now marvellously complex, and the progressive steps necessary for creating the initial sequence and chain-length-specific polymer templates are lost to time. Here we design and construct dynamic polymerization networks that exploit metastable prion cross-β phases. Mixed-phase environments have been used for constructing synthetic polymers, but these dynamic phases emerge naturally from the growing peptide oligomers and create environments suitable both to nucleate assembly and select for ordered templates. The resulting templates direct the amplification of a phase containing only chain-length-specific peptide-like oligomers. Such multi-phase biopolymer dynamics reveal pathways for the emergence, self-selection and amplification of chain-length- and possibly sequence-specific biopolymers.

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

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White, Forest M.; Wolf-Yadlin, Alejandro

2016-06-01

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

Bank-firm credit network in Japan: an analysis of a bipartite network.

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Marotta, Luca; Miccichè, Salvatore; Fujiwara, Yoshi; Iyetomi, Hiroshi; Aoyama, Hideaki; Gallegati, Mauro; Mantegna, Rosario N

2015-01-01

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-year-long analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms.

Antagonistic Phenomena in Network Dynamics

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Motter, Adilson E.; Timme, Marc

2018-03-01

Recent research on the network modeling of complex systems has led to a convenient representation of numerous natural, social, and engineered systems that are now recognized as networks of interacting parts. Such systems can exhibit a wealth of phenomena that not only cannot be anticipated from merely examining their parts, as per the textbook definition of complexity, but also challenge intuition even when considered in the context of what is now known in network science. Here, we review the recent literature on two major classes of such phenomena that have far-reaching implications: (a) antagonistic responses to changes of states or parameters and (b) coexistence of seemingly incongruous behaviors or properties - both deriving from the collective and inherently decentralized nature of the dynamics. They include effects as diverse as negative compressibility in engineered materials, rescue interactions in biological networks, negative resistance in fluid networks, and the Braess paradox occurring across transport and supply networks. They also include remote synchronization, chimera states, and the converse of symmetry breaking in brain, power-grid, and oscillator networks as well as remote control in biological and bioinspired systems. By offering a unified view of these various scenarios, we suggest that they are representative of a yet broader class of unprecedented network phenomena that ought to be revealed and explained by future research.

Pinning adaptive synchronization of a class of uncertain complex dynamical networks with multi-link against network deterioration

International Nuclear Information System (INIS)

Li, Lixiang; Li, Weiwei; Kurths, Jürgen; Luo, Qun; Yang, Yixian; Li, Shudong

2015-01-01

For the reason that the uncertain complex dynamic network with multi-link is quite close to various practical networks, there is superiority in the fields of research and application. In this paper, we focus upon pinning adaptive synchronization for uncertain complex dynamic networks with multi-link against network deterioration. The pinning approach can be applied to adapt uncertain coupling factors of deteriorated networks which can compensate effects of uncertainty. Several new synchronization criterions for networks with multi-link are derived, which ensure the synchronized states to be local or global stable with uncertainty and deterioration. Results of simulation are shown to demonstrate the feasibility and usefulness of our method

Dynamic Evolution of Financial Network and its Relation to Economic Crises

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Gao, Ya-Chun; Wei, Zong-Wen; Wang, Bing-Hong

2013-02-01

The static topology properties of financial networks have been widely investigated since the work done by Mantegna, yet their dynamic evolution with time is little considered. In this paper, we comprehensively study the dynamic evolution of financial network by a sliding window technique. The vertices and edges of financial network are represented by the stocks from S&P500 components and correlations between pairs of daily returns of price fluctuation, respectively. Furthermore, the duration of stock price fluctuation, spanning from January 4, 1985 to September 14, 2009, makes us to carefully observe the relation between the dynamic topological properties and big financial crashes. The empirical results suggest that the financial network has the robust small-world property when the time evolves, and the topological structure drastically changes when the big financial crashes occur. This correspondence between the dynamic evolution of financial network and big financial crashes may provide a novel view to understand the origin of economic crisis.

Network analysis shining light on parasite ecology and diversity.

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Poulin, Robert

2010-10-01

The vast number of species making up natural communities, and the myriad interactions among them, pose great difficulties for the study of community structure, dynamics and stability. Borrowed from other fields, network analysis is making great inroads in community ecology and is only now being applied to host-parasite interactions. It allows a complex system to be examined in its entirety, as opposed to one or a few components at a time. This review explores what network analysis is and how it can be used to investigate parasite ecology. It also summarizes the first findings to emerge from network analyses of host-parasite interactions and identifies promising future directions made possible by this approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

Science.gov (United States)

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Network Unfolding Map by Vertex-Edge Dynamics Modeling.

Science.gov (United States)

Verri, Filipe Alves Neto; Urio, Paulo Roberto; Zhao, Liang

2018-02-01

The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semisupervised learning problems. Three actions govern the particles' dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model evolution consists of sets of edges arranged by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove that there exists a deterministic version with largely reduced computational complexity; specifically, with linear growth. Furthermore, the edge domination process corresponds to an unfolding map in such way that edges "stretch" and "shrink" according to the vertex-edge dynamics. Consequently, the unfolding effect summarizes the relevant relationships between vertices and the uncovered data classes. The proposed model captures important details of connectivity patterns over the vertex-edge dynamics evolution, in contrast to the previous approaches, which focused on only vertex or only edge dynamics. Computer simulations reveal that the new model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and overlapping structures of data.

On the number of different dynamics in Boolean networks with deterministic update schedules.

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Aracena, J; Demongeot, J; Fanchon, E; Montalva, M

2013-04-01

Deterministic Boolean networks are a type of discrete dynamical systems widely used in the modeling of genetic networks. The dynamics of such systems is characterized by the local activation functions and the update schedule, i.e., the order in which the nodes are updated. In this paper, we address the problem of knowing the different dynamics of a Boolean network when the update schedule is changed. We begin by proving that the problem of the existence of a pair of update schedules with different dynamics is NP-complete. However, we show that certain structural properties of the interaction diagraph are sufficient for guaranteeing distinct dynamics of a network. In [1] the authors define equivalence classes which have the property that all the update schedules of a given class yield the same dynamics. In order to determine the dynamics associated to a network, we develop an algorithm to efficiently enumerate the above equivalence classes by selecting a representative update schedule for each class with a minimum number of blocks. Finally, we run this algorithm on the well known Arabidopsis thaliana network to determine the full spectrum of its different dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

The dynamical modeling and simulation analysis of the recommendation on the user-movie network

Science.gov (United States)

Zhang, Shujuan; Jin, Zhen; Zhang, Juan

2016-12-01

At present, most research about the recommender system is based on graph theory and algebraic methods, but these methods cannot predict the evolution of the system with time under the recommendation method, and cannot dynamically analyze the long-term utility of the recommendation method. However, these two aspects can be studied by the dynamical method, which essentially investigates the intrinsic evolution mechanism of things, and is widely used to study a variety of actual problems. So, in this paper, network dynamics is used to study the recommendation on the user-movie network, which consists of users and movies, and the movies are watched either by the personal search or through the recommendation. Firstly, dynamical models are established to characterize the personal search and the system recommendation mechanism: the personal search model, the random recommendation model, the preference recommendation model, the degree recommendation model and the hybrid recommendation model. The rationality of the models established is verified by comparing the stochastic simulation with the numerical simulation. Moreover, the validity of the recommendation methods is evaluated by studying the movie degree, which is defined as the number of the movie that has been watched. Finally, we combine the personal search and the recommendation to establish a more general model. The change of the average degree of all the movies is given with the strength of the recommendation. Results show that for each recommendation method, the change of the movie degree is different, and is related to the initial degree of movies, the adjacency matrix A representing the relation between users and movies, the time t. Additionally, we find that in a long time, the degree recommendation is not as good as that in a short time, which fully demonstrates the advantage of the dynamical method. For the whole user-movie system, the preference recommendation is the best.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport.

Science.gov (United States)

Riascos, A P; Mateos, José L

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Practical synchronization on complex dynamical networks via optimal pinning control

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Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

2015-07-01

We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

Dynamics of Moment Neuronal Networks with Intra- and Inter-Interactions

Directory of Open Access Journals (Sweden)

Xuyan Xiang

2015-01-01

Full Text Available A framework of moment neuronal networks with intra- and inter-interactions is presented. It is to show how the spontaneous activity is propagated across the homogeneous and heterogeneous network. The input-output firing relationship and the stability are first explored for a homogeneous network. For heterogeneous network without the constraint of the correlation coefficients between neurons, a more sophisticated dynamics is then explored. With random interactions, the network gets easily synchronized. However, desynchronization is produced by a lateral interaction such as Mexico hat function. It is the external intralayer input unit that offers a more sophisticated and unexpected dynamics over the predecessors. Hence, the work further opens up the possibility of carrying out a stochastic computation in neuronal networks.

Dynamic burstiness of word-occurrence and network modularity in textbook systems

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Cui, Xue-Mei; Yoon, Chang No; Youn, Hyejin; Lee, Sang Hoon; Jung, Jean S.; Han, Seung Kee

2017-12-01

We show that the dynamic burstiness of word occurrence in textbook systems is attributed to the modularity of the word association networks. At first, a measure of dynamic burstiness is introduced to quantify burstiness of word occurrence in a textbook. The advantage of this measure is that the dynamic burstiness is decomposable into two contributions: one coming from the inter-event variance and the other from the memory effects. Comparing network structures of physics textbook systems with those of surrogate random textbooks without the memory or variance effects are absent, we show that the network modularity increases systematically with the dynamic burstiness. The intra-connectivity of individual word representing the strength of a tie with which a node is bound to a module accordingly increases with the dynamic burstiness, suggesting individual words with high burstiness are strongly bound to one module. Based on the frequency and dynamic burstiness, physics terminology is classified into four categories: fundamental words, topical words, special words, and common words. In addition, we test the correlation between the dynamic burstiness of word occurrence and network modularity using a two-state model of burst generation.

Non-homogeneous dynamic Bayesian networks for continuous data

NARCIS (Netherlands)

Grzegorczyk, Marco; Husmeier, Dirk