Copelli, M.; Campos, P. R. A.
2007-04-01
When a simple excitable system is continuously stimulated by a Poissonian external source, the response function (mean activity versus stimulus rate) generally shows a linear saturating shape. This is experimentally verified in some classes of sensory neurons, which accordingly present a small dynamic range (defined as the interval of stimulus intensity which can be appropriately coded by the mean activity of the excitable element), usually about one or two decades only. The brain, on the other hand, can handle a significantly broader range of stimulus intensity, and a collective phenomenon involving the interaction among excitable neurons has been suggested to account for the enhancement of the dynamic range. Since the role of the pattern of such interactions is still unclear, here we investigate the performance of a scale-free (SF) network topology in this dynamic range problem. Specifically, we study the transfer function of disordered SF networks of excitable Greenberg-Hastings cellular automata. We observe that the dynamic range is maximum when the coupling among the elements is critical, corroborating a general reasoning recently proposed. Although the maximum dynamic range yielded by general SF networks is slightly worse than that of random networks, for special SF networks which lack loops the enhancement of the dynamic range can be dramatic, reaching nearly five decades. In order to understand the role of loops on the transfer function we propose a simple model in which the density of loops in the network can be gradually increased, and show that this is accompanied by a gradual decrease of dynamic range.
Emergence of Scale-Free Syntax Networks
Corominas-Murtra, Bernat; Valverde, Sergi; Solé, Ricard V.
The evolution of human language allowed the efficient propagation of nongenetic information, thus creating a new form of evolutionary change. Language development in children offers the opportunity of exploring the emergence of such complex communication system and provides a window to understanding the transition from protolanguage to language. Here we present the first analysis of the emergence of syntax in terms of complex networks. A previously unreported, sharp transition is shown to occur around two years of age from a (pre-syntactic) tree-like structure to a scale-free, small world syntax network. The observed combinatorial patterns provide valuable data to understand the nature of the cognitive processes involved in the acquisition of syntax, introducing a new ingredient to understand the possible biological endowment of human beings which results in the emergence of complex language. We explore this problem by using a minimal, data-driven model that is able to capture several statistical traits, but some key features related to the emergence of syntactic complexity display important divergences.
Power Laws, Scale-Free Networks and Genome Biology
Koonin, Eugene V; Karev, Georgy P
2006-01-01
Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...
Chaotic Modes in Scale Free Opinion Networks
Kusmartsev, Feo V.; Kürten, Karl E.
2010-12-01
In this paper, we investigate processes associated with formation of public opinion in varies directed random, scale free and small-world social networks. The important factor of the opinion formation is the existence of contrarians which were discovered by Granovetter in various social psychology experiments1,2,3 long ago and later introduced in sociophysics by Galam.4 When the density of contrarians increases the system behavior drastically changes at some critical value. At high density of contrarians the system can never arrive to a consensus state and periodically oscillates with different periods depending on specific structure of the network. At small density of the contrarians the behavior is manifold. It depends primary on the initial state of the system. If initially the majority of the population agrees with each other a state of stable majority may be easily reached. However when originally the population is divided in nearly equal parts consensus can never be reached. We model the emergence of collective decision making by considering N interacting agents, whose opinions are described by two state Ising spin variable associated with YES and NO. We show that the dynamical behaviors are very sensitive not only to the density of the contrarians but also to the network topology. We find that a phase of social chaos may arise in various dynamical processes of opinion formation in many realistic models. We compare the prediction of the theory with data describing the dynamics of the average opinion of the USA population collected on a day-by-day basis by varies media sources during the last six month before the final Obama-McCain election. The qualitative ouctome is in reasonable agreement with the prediction of our theory. In fact, the analyses of these data made within the paradigm of our theory indicates that even in this campaign there were chaotic elements where the public opinion migrated in an unpredictable chaotic way. The existence of such a phase
Exact Solutions of a Generalized Weighted Scale Free Network
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Li Tan
2013-01-01
Full Text Available We investigate a class of generalized weighted scale-free networks, where the new vertex connects to m pairs of vertices selected preferentially. The key contribution of this paper is that, from the standpoint of random processes, we provide rigorous analytic solutions for the steady state distributions, including the vertex degree distribution, the vertex strength distribution and the edge weight distribution. Numerical simulations indicate that this network model yields three power law distributions for the vertex degrees, vertex strengths and edge weights, respectively.
Statistical mechanics of scale-free gene expression networks
Gross, Eitan
2012-12-01
The gene co-expression networks of many organisms including bacteria, mice and man exhibit scale-free distribution. This heterogeneous distribution of connections decreases the vulnerability of the network to random attacks and thus may confer the genetic replication machinery an intrinsic resilience to such attacks, triggered by changing environmental conditions that the organism may be subject to during evolution. This resilience to random attacks comes at an energetic cost, however, reflected by the lower entropy of the scale-free distribution compared to the more homogenous, random network. In this study we found that the cell cycle-regulated gene expression pattern of the yeast Saccharomyces cerevisiae obeys a power-law distribution with an exponent α = 2.1 and an entropy of 1.58. The latter is very close to the maximal value of 1.65 obtained from linear optimization of the entropy function under the constraint of a constant cost function, determined by the average degree connectivity . We further show that the yeast's gene expression network can achieve scale-free distribution in a process that does not involve growth but rather via re-wiring of the connections between nodes of an ordered network. Our results support the idea of an evolutionary selection, which acts at the level of the protein sequence, and is compatible with the notion of greater biological importance of highly connected nodes in the protein interaction network. Our constrained re-wiring model provides a theoretical framework for a putative thermodynamically driven evolutionary selection process.
Scale-free networks as entropy competition
Sanchirico, Antonio; Fiorentino, Mauro
2008-10-01
Complex networks describe several and different real-world systems consisting of a number of interacting elements. A very important characteristic of such networks is the degree distribution that strongly controls their behavior. Based on statistical mechanics, three classes of uncorrelated complex networks are identified here, depending on the role played by the connectivities amongst elements. In particular, by identifying the connectivities of a node with the number of its nearest neighbors, we show that the power law is the most probable degree distribution that both nodes and neighbors, in a reciprocal competition, assume when the respective entropy functions reach their maxima, under mutual constraint. As a result, we obtain scaling exponent values as a function of the structural characteristics of the whole network. Moreover, our approach sheds light on the exponential and Poissonian degree distributions, derived, respectively, when connectivities are thought of as degenerated connections or as half-edges. Thus, all three classes of degree distributions are derived, starting from a common principle and leading to a general and unified framework for investigating the network structure.
Evolution of vocabulary on scale-free and random networks
Kalampokis, Alkiviadis; Kosmidis, Kosmas; Argyrakis, Panos
2007-06-01
We examine the evolution of the vocabulary of a group of individuals (linguistic agents) on a scale-free network, using Monte Carlo simulations and assumptions from evolutionary game theory. It is known that when the agents are arranged in a two-dimensional lattice structure and interact by diffusion and encounter, then their final vocabulary size is the maximum possible. Knowing all available words is essential in order to increase the probability to “survive” by effective reproduction. On scale-free networks we find a different result. It is not necessary to learn the entire vocabulary available. Survival chances are increased by using the vocabulary of the “hubs” (nodes with high degree). The existence of the “hubs” in a scale-free network is the source of an additional important fitness generating mechanism.
A scale-free neural network for modelling neurogenesis
Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.
2006-11-01
In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.
An optimal routing strategy on scale-free networks
Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Qi, Zhaohui; Zhao, Yongbin
Traffic is one of the most fundamental dynamical processes in networked systems. With the traditional shortest path routing (SPR) protocol, traffic congestion is likely to occur on the hub nodes on scale-free networks. In this paper, we propose an improved optimal routing (IOR) strategy which is based on the betweenness centrality and the degree centrality of nodes in the scale-free networks. With the proposed strategy, the routing paths can accurately bypass hub nodes in the network to enhance the transport efficiency. Simulation results show that the traffic capacity as well as some other indexes reflecting transportation efficiency are further improved with the IOR strategy. Owing to the significantly improved traffic performance, this study is helpful to design more efficient routing strategies in communication or transportation systems.
Metric clusters in evolutionary games on scale-free networks.
Kleineberg, Kaj-Kolja
2017-12-01
The evolution of cooperation in social dilemmas in structured populations has been studied extensively in recent years. Whereas many theoretical studies have found that a heterogeneous network of contacts favors cooperation, the impact of spatial effects in scale-free networks is still not well understood. In addition to being heterogeneous, real contact networks exhibit a high mean local clustering coefficient, which implies the existence of an underlying metric space. Here we show that evolutionary dynamics in scale-free networks self-organize into spatial patterns in the underlying metric space. The resulting metric clusters of cooperators are able to survive in social dilemmas as their spatial organization shields them from surrounding defectors, similar to spatial selection in Euclidean space. We show that under certain conditions these metric clusters are more efficient than the most connected nodes at sustaining cooperation and that heterogeneity does not always favor-but can even hinder-cooperation in social dilemmas.
Characterizing the intrinsic correlations of scale-free networks
de Brito, J B; Moreira, A A; Andrade, J S
2015-01-01
Very often, when studying topological or dynamical properties of random scale-free networks, it is tacitly assumed that degree-degree correlations are not present. However, simple constraints, such as the absence of multiple edges and self-loops, can give rise to intrinsic correlations in these structures. In the same way that Fermionic correlations in thermodynamic systems are relevant only in the limit of low temperature, the intrinsic correlations in scale-free networks are relevant only when the extreme values for the degrees grow faster than the square-root of the network size. In this situation, these correlations can significantly affect the dependence of the average degree of the nearest neighbors of a given vertice on this vertices's degree. Here, we introduce an analytical approach that is capable to predict the functional form of this property. Moreover, our results indicate that random scale-free networks models are not self-averaging, that is, the second moment of their degree distribution may va...
Lower bound of assortativity coefficient in scale-free networks
Yang, Dan; Pan, Liming; Zhou, Tao
2017-03-01
The degree-degree correlation is important in understanding the structural organization of a network and dynamics upon a network. Such correlation is usually measured by the assortativity coefficient r, with natural bounds r ∈ [ - 1 , 1 ] . For scale-free networks with power-law degree distribution p ( k ) ˜ k - γ , we analytically obtain the lower bound of assortativity coefficient in the limit of large network size, which is not -1 but dependent on the power-law exponent γ. This work challenges the validation of the assortativity coefficient in heterogeneous networks, suggesting that one cannot judge whether a network is positively or negatively correlated just by looking at its assortativity coefficient alone.
Innovation diffusion equations on correlated scale-free networks
Bertotti, M. L.; Brunner, J.; Modanese, G.
2016-07-01
We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.
Mean field analysis of algorithms for scale-free networks in molecular biology.
Konini, S; Janse van Rensburg, E J
2017-01-01
The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k-γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks).
Traffic properties for stochastic routings on scale-free networks
Hayashi, Yukio
2011-01-01
For realistic scale-free networks, we investigate the traffic properties of stochastic routing inspired by a zero-range process known in statistical physics. By parameters $\\alpha$ and $\\delta$, this model controls degree-dependent hopping of packets and forwarding of packets with higher performance at more busy nodes. Through a theoretical analysis and numerical simulations, we derive the condition for the concentration of packets at a few hubs. In particular, we show that the optimal $\\alpha$ and $\\delta$ are involved in the trade-off between a detour path for $\\alpha 0$; In the low-performance regime at a small $\\delta$, the wandering path for $\\alpha 0$ and $\\alpha < 0$ is small, neither the wandering long path with short wait trapped at nodes ($\\alpha = -1$), nor the short hopping path with long wait trapped at hubs ($\\alpha = 1$) is advisable. A uniformly random walk ($\\alpha = 0$) yields slightly better performance. We also discuss the congestion phenomena in a more complicated situation with pack...
Small-World and Scale-Free Network Models for IoT Systems
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Insoo Sohn
2017-01-01
Full Text Available It is expected that Internet of Things (IoT revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.
Evolution of Scale-Free Wireless Sensor Networks with Feature of Small-World Networks
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Ying Duan
2017-01-01
Full Text Available Scale-free network and small-world network are the most impacting discoveries in the complex networks theories and have already been successfully proved to be highly effective in improving topology structures of wireless sensor networks. However, currently both theories are not jointly applied to have further improvements in the generation of WSN topologies. Therefore, this paper proposes a cluster-structured evolution model of WSNs considering the characteristics of both networks. With introduction of energy sensitivity and maximum limitation of degrees that a cluster head could have, the performance of our model can be ensured. In order to give an overall assessment of lifting effects of shortcuts, four placement schemes of shortcuts are analyzed. The characteristics of small-world network and scale-free network of our model are proved via theoretical derivation and simulations. Besides, we find that, by introducing shortcuts into scale-free wireless sensor network, the performance of the network can be improved concerning energy-saving and invulnerability, and we discover that the schemes constructing shortcuts between cluster heads and the sink node have better promoted effects than the scheme building shortcuts between pairs of cluster heads, and the schemes based on the preferential principle are superior to the schemes based on the random principle.
Uncovering disassortativity in large scale-free networks
Litvak, Nelli; van der Hofstad, Remco
2013-01-01
Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, and social and biological networks, are often characterized by degree-degree dependencies between neighboring nodes. In this paper, we propose a new way of measuring degree-degree dependencies. One of the
Some scale-free networks could be robust under selective node attacks
Zheng, Bojin; Huang, Dan; Li, Deyi; Chen, Guisheng; Lan, Wenfei
2011-04-01
It is a mainstream idea that scale-free network would be fragile under the selective attacks. Internet is a typical scale-free network in the real world, but it never collapses under the selective attacks of computer viruses and hackers. This phenomenon is different from the deduction of the idea above because this idea assumes the same cost to delete an arbitrary node. Hence this paper discusses the behaviors of the scale-free network under the selective node attack with different cost. Through the experiments on five complex networks, we show that the scale-free network is possibly robust under the selective node attacks; furthermore, the more compact the network is, and the larger the average degree is, then the more robust the network is; with the same average degrees, the more compact the network is, the more robust the network is. This result would enrich the theory of the invulnerability of the network, and can be used to build robust social, technological and biological networks, and also has the potential to find the target of drugs.
The spread of computer viruses over a reduced scale-free network
Yang, Lu-Xing; Yang, Xiaofan
2014-02-01
Due to the high dimensionality of an epidemic model of computer viruses over a general scale-free network, it is difficult to make a close study of its dynamics. In particular, it is extremely difficult, if not impossible, to prove the global stability of its viral equilibrium, if any. To overcome this difficulty, we suggest to simplify a general scale-free network by partitioning all of its nodes into two classes: higher-degree nodes and lower-degree nodes, and then equating the degrees of all higher-degree nodes and all lower-degree nodes, respectively, yielding a reduced scale-free network. We then propose an epidemic model of computer viruses over a reduced scale-free network. A theoretical analysis reveals that the proposed model is bound to have a globally stable viral equilibrium, implying that any attempt to eradicate network viruses would prove unavailing. As a result, the next best thing we can do is to restrain virus prevalence. Based on an analysis of the impact of different model parameters on virus prevalence, some practicable measures are recommended to contain virus spreading. The work in this paper adequately justifies the idea of reduced scale-free networks.
An adaptive routing scheme in scale-free networks
Ben Haddou, Nora; Ez-Zahraouy, Hamid; Benyoussef, Abdelilah
2015-05-01
We suggest an optimal form of traffic awareness already introduced as a routing protocol which combines structural and local dynamic properties of the network to determine the followed path between source and destination of the packet. Instead of using the shortest path, we incorporate the "efficient path" in the protocol and we propose a new parameter α that controls the contribution of the queue in the routing process. Compared to the original model, the capacity of the network can be improved more than twice when using the optimal conditions of our model. Moreover, the adjustment of the proposed parameter allows the minimization of the travel time.
A hybrid queuing strategy for network traffic on scale-free networks
Cai, Kai-Quan; Yu, Lu; Zhu, Yan-Bo
2017-02-01
In this paper, a hybrid queuing strategy (HQS) is proposed in traffic dynamics model on scale-free networks, where the delivery priority of packets in the queue is related to their distance to destination and the queue length of next jump. We compare the performance of the proposed HQS with that of the traditional first-in-first-out (FIFO) queuing strategy and the shortest-remaining-path-first (SRPF) queuing strategy proposed by Du et al. It is observed that the network traffic efficiency utilizing HQS with suitable value of parameter h can be further improved in the congestion state. Our work provides new insights for the understanding of the networked-traffic systems.
Critical behavior of the contact process in annealed scale-free networks
Noh, Jae Dong; Park, Hyunggyu
2008-01-01
Critical behavior of the contact process is studied in annealed scale-free networks by mapping it on the random walk problem. We obtain the analytic results for the critical scaling, using the event-driven dynamics approach. These results are confirmed by numerical simulations. The disorder fluctuation induced by the sampling disorder in annealed networks is also explored. Finally, we discuss over the discrepancy of the finite-size-scaling theory in annealed and quenched networks in spirit of...
Emergence of super cooperation of prisoner's dilemma games on scale-free networks.
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Angsheng Li
Full Text Available Recently, the authors proposed a quantum prisoner's dilemma game based on the spatial game of Nowak and May, and showed that the game can be played classically. By using this idea, we proposed three generalized prisoner's dilemma (GPD, for short games based on the weak Prisoner's dilemma game, the full prisoner's dilemma game and the normalized Prisoner's dilemma game, written by GPDW, GPDF and GPDN respectively. Our games consist of two players, each of which has three strategies: cooperator (C, defector (D and super cooperator (denoted by Q, and have a parameter γ to measure the entangled relationship between the two players. We found that our generalised prisoner's dilemma games have new Nash equilibrium principles, that entanglement is the principle of emergence and convergence (i.e., guaranteed emergence of super cooperation in evolutions of our generalised prisoner's dilemma games on scale-free networks, that entanglement provides a threshold for a phase transition of super cooperation in evolutions of our generalised prisoner's dilemma games on scale-free networks, that the role of heterogeneity of the scale-free networks in cooperations and super cooperations is very limited, and that well-defined structures of scale-free networks allow coexistence of cooperators and super cooperators in the evolutions of the weak version of our generalised prisoner's dilemma games.
Emergence of Super Cooperation of Prisoner’s Dilemma Games on Scale-Free Networks
Li, Angsheng; Yong, Xi
2015-01-01
Recently, the authors proposed a quantum prisoner’s dilemma game based on the spatial game of Nowak and May, and showed that the game can be played classically. By using this idea, we proposed three generalized prisoner’s dilemma (GPD, for short) games based on the weak Prisoner’s dilemma game, the full prisoner’s dilemma game and the normalized Prisoner’s dilemma game, written by GPDW, GPDF and GPDN respectively. Our games consist of two players, each of which has three strategies: cooperator (C), defector (D) and super cooperator (denoted by Q), and have a parameter γ to measure the entangled relationship between the two players. We found that our generalised prisoner’s dilemma games have new Nash equilibrium principles, that entanglement is the principle of emergence and convergence (i.e., guaranteed emergence) of super cooperation in evolutions of our generalised prisoner’s dilemma games on scale-free networks, that entanglement provides a threshold for a phase transition of super cooperation in evolutions of our generalised prisoner’s dilemma games on scale-free networks, that the role of heterogeneity of the scale-free networks in cooperations and super cooperations is very limited, and that well-defined structures of scale-free networks allow coexistence of cooperators and super cooperators in the evolutions of the weak version of our generalised prisoner’s dilemma games. PMID:25643279
Robustness of scale-free networks to cascading failures induced by fluctuating loads.
Mizutaka, Shogo; Yakubo, Kousuke
2015-07-01
Taking into account the fact that overload failures in real-world functional networks are usually caused by extreme values of temporally fluctuating loads that exceed the allowable range, we study the robustness of scale-free networks against cascading overload failures induced by fluctuating loads. In our model, loads are described by random walkers moving on a network and a node fails when the number of walkers on the node is beyond the node capacity. Our results obtained by using the generating function method show that scale-free networks are more robust against cascading overload failures than Erdős-Rényi random graphs with homogeneous degree distributions. This conclusion is contrary to that predicted by previous works, which neglect the effect of fluctuations of loads.
Walking Across Wikipedia: A Scale-Free Network Model of Semantic Memory Retrieval
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Graham William Thompson
2014-02-01
Full Text Available Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like animals are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.
Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.
Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han
2013-11-01
Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.
Sparse cliques trump scale-free networks in coordination and competition
Gianetto, David A.; Heydari, Babak
2016-02-01
Cooperative behavior, a natural, pervasive and yet puzzling phenomenon, can be significantly enhanced by networks. Many studies have shown how global network characteristics affect cooperation; however, it is difficult to understand how this occurs based on global factors alone, low-level network building blocks, or motifs are necessary. In this work, we systematically alter the structure of scale-free and clique networks and show, through a stochastic evolutionary game theory model, that cooperation on cliques increases linearly with community motif count. We further show that, for reactive stochastic strategies, network modularity improves cooperation in the anti-coordination Snowdrift game and the Prisoner’s Dilemma game but not in the Stag Hunt coordination game. We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs are used. On the flip side, clique graphs are highly cooperative across social environments. Adding cycles to the acyclic scale-free graph increases cooperation when multiple games are considered; however, cycles have the opposite effect on how forgiving agents are when playing the Prisoner’s Dilemma game.
Modeling Peer-to-Peer Botnet on Scale-Free Network
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Liping Feng
2014-01-01
Full Text Available Peer-to-peer (P2P botnets have emerged as one of the serious threats to Internet security. To prevent effectively P2P botnet, in this paper, a mathematical model which combines the scale-free trait of Internet with the formation of P2P botnet is presented. Explicit mathematical analysis demonstrates that the model has a globally stable endemic equilibrium when infection rate is greater than a critical value. Meanwhile, we find that, in scale-free network, the critical value is very little. Hence, it is unrealistic to completely dispel the P2P botnet. Numerical simulations show that one can take effective countermeasures to reduce the scale of P2P botnet or delay its outbreak. Our findings can provide meaningful instruction to network security management.
Explosive synchronization in clustered scale-free networks: Revealing the existence of chimera state
Berec, V.
2016-02-01
The collective dynamics of Kuramoto oscillators with a positive correlation between the incoherent and fully coherent domains in clustered scale-free networks is studied. Emergence of chimera states for the onsets of explosive synchronization transition is observed during an intermediate coupling regime when degree-frequency correlation is established for the hubs with the highest degrees. Diagnostic of the abrupt synchronization is revealed by the intrinsic spectral properties of the network graph Laplacian encoded in the heterogeneous phase space manifold, through extensive analytical investigation, presenting realistic MC simulations of nonlocal interactions in discrete time dynamics evolving on the network.
Analysis of Average Shortest-Path Length of Scale-Free Network
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Guoyong Mao
2013-01-01
Full Text Available Computing the average shortest-path length of a large scale-free network needs much memory space and computation time. Hence, parallel computing must be applied. In order to solve the load-balancing problem for coarse-grained parallelization, the relationship between the computing time of a single-source shortest-path length of node and the features of node is studied. We present a dynamic programming model using the average outdegree of neighboring nodes of different levels as the variable and the minimum time difference as the target. The coefficients are determined on time measurable networks. A native array and multimap representation of network are presented to reduce the memory consumption of the network such that large networks can still be loaded into the memory of each computing core. The simplified load-balancing model is applied on a network of tens of millions of nodes. Our experiment shows that this model can solve the load-imbalance problem of large scale-free network very well. Also, the characteristic of this model can meet the requirements of networks with ever-increasing complexity and scale.
Utilizing Maximal Independent Sets as Dominating Sets in Scale-Free Networks
Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.
Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.
Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study.
Kim, Do-Hyun; Park, Jinha; Kahng, Byungnam
2017-01-01
The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of O(N), where N is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural networks has been further developed toward realistic neural networks using analog neurons, spiking neurons, etc. Nevertheless, those advances are based on fully connected networks, which are inconsistent with recent experimental discovery that the number of connections of each neuron seems to be heterogeneous, following a heavy-tailed distribution. Motivated by this observation, we consider the Hopfield model on scale-free networks and obtain a different pattern of associative memory retrieval from that obtained on the fully connected network: the storage capacity becomes tremendously enhanced but with some error in the memory retrieval, which appears as the heterogeneity of the connections is increased. Moreover, the error rates are also obtained on several real neural networks and are indeed similar to that on scale-free model networks.
Effects of maximum node degree on computer virus spreading in scale-free networks
Bamaarouf, O.; Ould Baba, A.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.
2017-10-01
The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible-Infected-External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree (Kc), below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.
Scale-free models for the structure of business firm networks.
Kitsak, Maksim; Riccaboni, Massimo; Havlin, Shlomo; Pammolli, Fabio; Stanley, H Eugene
2010-03-01
We study firm collaborations in the life sciences and the information and communication technology sectors. We propose an approach to characterize industrial leadership using k -shell decomposition, with top-ranking firms in terms of market value in higher k -shell layers. We find that the life sciences industry network consists of three distinct components: a "nucleus," which is a small well-connected subgraph, "tendrils," which are small subgraphs consisting of small degree nodes connected exclusively to the nucleus, and a "bulk body," which consists of the majority of nodes. Industrial leaders, i.e., the largest companies in terms of market value, are in the highest k -shells of both networks. The nucleus of the life sciences sector is very stable: once a firm enters the nucleus, it is likely to stay there for a long time. At the same time we do not observe the above three components in the information and communication technology sector. We also conduct a systematic study of these three components in random scale-free networks. Our results suggest that the sizes of the nucleus and the tendrils in scale-free networks decrease as the exponent of the power-law degree distribution lambda increases, and disappear for lambda>or=3 . We compare the k -shell structure of random scale-free model networks with two real-world business firm networks in the life sciences and in the information and communication technology sectors. We argue that the observed behavior of the k -shell structure in the two industries is consistent with the coexistence of both preferential and random agreements in the evolution of industrial networks.
Robustness of cooperation on scale-free networks under continuous topological change
Ichinose, Genki; Tanizawa, Toshihiro
2013-01-01
In this paper, we numerically investigate the robustness of cooperation clusters in prisoner's dilemma played on scale-free networks, where their network topologies change by continuous removal and addition of nodes. Each of these removal and addition can be either random or intentional. We therefore have four different strategies in changing network topology: random removal and random addition (RR), random removal and preferential addition (RP), targeted removal and random addition (TR), and targeted removal and preferential addition (TP). We find that cooperation clusters are the most fragile against TR, while they are the most robust against RP even in high temptation coefficients for defect. The effect of the degree mixing pattern of the network is not the primary factor for the robustness of cooperation under continuous change in network topology due to consequential removal and addition of nodes, which is quite different from the cases observed in static networks. Cooperation clusters become more robust...
An improved local immunization strategy for scale-free networks with a high degree of clustering
Xia, Lingling; Jiang, Guoping; Song, Yurong; Song, Bo
2017-01-01
The design of immunization strategies is an extremely important issue for disease or computer virus control and prevention. In this paper, we propose an improved local immunization strategy based on node's clustering which was seldom considered in the existing immunization strategies. The main aim of the proposed strategy is to iteratively immunize the node which has a high connectivity and a low clustering coefficient. To validate the effectiveness of our strategy, we compare it with two typical local immunization strategies on both real and artificial networks with a high degree of clustering. Simulations on these networks demonstrate that the performance of our strategy is superior to that of two typical strategies. The proposed strategy can be regarded as a compromise between computational complexity and immune effect, which can be widely applied in scale-free networks of high clustering, such as social network, technological networks and so on. In addition, this study provides useful hints for designing optimal immunization strategy for specific network.
Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter
2015-01-01
One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796
Synchronization in scale-free networks: The role of finite-size effects
Torres, D.; Di Muro, M. A.; La Rocca, C. E.; Braunstein, L. A.
2015-06-01
Synchronization problems in complex networks are very often studied by researchers due to their many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, scale-free networks with degree distribution P(k)∼ k-λ , are widely used in research since they are ubiquitous in Nature and other real systems. In this paper we focus on the surface relaxation growth model in scale-free networks with 2.5< λ <3 , and study the scaling behavior of the fluctuations, in the steady state, with the system size N. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of N=N* that depends on λ: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above N* , the fluctuations decrease with λ, which means that the synchronization of the system improves as λ increases. We explain this crossover analyzing the role of the network's heterogeneity produced by the system size N and the exponent of the degree distribution.
Synchronization in Scale Free networks: The role of finite size effects
Torres, Débora; La Rocca, Cristian E; Braunstein, Lidia A
2015-01-01
Synchronization problems in complex networks are very often studied by researchers due to its many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, Scale Free networks with degree distribution $P(k)\\sim k^{-\\lambda}$, are widely used in research since they are ubiquitous in nature and other real systems. In this paper we focus on the surface relaxation growth model in Scale Free networks with $2.5< \\lambda <3$, and study the scaling behavior of the fluctuations, in the steady state, with the system size $N$. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of $N=N^*$ that depends on $\\lambda$: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above $N^{*}$, the fluctuations decrease with $\\lambda$, which means that the synchroniza...
Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks.
Piersa, Jaroslaw; Piekniewski, Filip; Schreiber, Tomasz
2010-11-01
In this paper, we provide theoretical and numerical analysis of a geometric activity flow network model which is aimed at explaining mathematically the scale-free functional graph self-organization phenomena emerging in complex nervous systems at a mesoscale level. In our model, each unit corresponds to a large number of neurons and may be roughly seen as abstracting the functional behavior exhibited by a single voxel under functional magnetic resonance imaging (fMRI). In the course of the dynamics, the units exchange portions of formal charge, which correspond to waves of activity in the underlying microscale neuronal circuit. The geometric model abstracts away the neuronal complexity and is mathematically tractable, which allows us to establish explicit results on its ground states and the resulting charge transfer graph modeling functional graph of the network. We show that, for a wide choice of parameters and geometrical setups, our model yields a scale-free functional connectivity with the exponent approaching 2, which is in agreement with previous empirical studies based on fMRI. The level of universality of the presented theory allows us to claim that the model does shed light on mesoscale functional self-organization phenomena of the nervous system, even without resorting to closer details of brain connectivity geometry which often remain unknown. The material presented here significantly extends our previous work where a simplified mean-field model in a similar spirit was constructed, ignoring the underlying network geometry.
Spreading dynamics of an e-commerce preferential information model on scale-free networks
Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding
2017-02-01
In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.
Optimal control strategy for a novel computer virus propagation model on scale-free networks
Zhang, Chunming; Huang, Haitao
2016-06-01
This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.
A Congestion Control Strategy for Power Scale-Free Communication Network
Directory of Open Access Journals (Sweden)
Min Xiang
2017-10-01
Full Text Available The scale-free topology of power communication network leads to more data flow in less hub nodes, which can cause local congestion. Considering the differences of the nodes’ delivery capacity and cache capacity, an integrated routing based on the communication service classification is proposed to reduce network congestion. In the power communication network, packets can be classified as key operational services (I-level and affairs management services (II-level. The shortest routing, which selects the path of the least hops, is adopted to transmit I-level packets. The load-balanced global dynamic routing, which uses the node’s queue length and delivery capacity to establish the cost function and chooses the path with minimal cost, is adopted to transmit II-level packets. The simulation results show that the integrated routing has a larger critical packet generation rate and can effectively reduce congestion.
Dynamics of epidemic spreading model with drug-resistant variation on scale-free networks
Wan, Chen; Li, Tao; Zhang, Wu; Dong, Jing
2018-03-01
Considering the influence of the virus' drug-resistant variation, a novel SIVRS (susceptible-infected-variant-recovered-susceptible) epidemic spreading model with variation characteristic on scale-free networks is proposed in this paper. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Studies show that the existence of disease-free equilibrium is determined by the basic reproductive number R0. The relationships between the basic reproductive number R0, the variation characteristic and the topology of the underlying networks are studied in detail. Furthermore, our studies prove the global stability of the disease-free equilibrium, the permanence of epidemic and the global attractivity of endemic equilibrium. Numerical simulations are performed to confirm the analytical results.
On the visualization of social and other scale-free networks.
Jia, Yuntao; Hoberock, Jared; Garland, Michael; Hart, John C
2008-01-01
This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network's underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.
Influence of dynamical condensation on epidemic spreading in scale-free networks.
Tang, Ming; Liu, Li; Liu, Zonghua
2009-01-01
Considering the accumulation phenomenon in public places, we investigate how the condensation of moving bosonic particles influences the epidemic spreading in scale-free metapopulation networks. Our mean-field theory shows that condensation can significantly enhance the effect of epidemic spreading and reduce the threshold for epidemic to survive, in contrast to the case of without condensation. In the stationary state, the number of infected particles increases with the degree k linearly when kk_{c}, where k_{c} denotes the crossover degree of the nodes with unity particle. The dependence of critical infective rate beta_{c} on the parameters k_{max}, micro, and delta, is figured out, where k_{max}, micro, and delta denote the largest degree, recovery rate, and jumping exponent, respectively. Numerical simulations have confirmed the theoretical predictions.
Statistical properties of Olami-Feder-Christensen model on Barabasi-Albert scale-free network
Tanaka, Hiroki; Hatano, Takahiro
2017-12-01
The Olami-Feder-Christensen model on the Barabasi-Albert type scale-free network is investigated in the context of statistical seismology. This simple model may be regarded as the interacting faults obeying power-law size distribution under two assumptions: (i) each node represents a distinct fault; (ii) the degree of a node is proportional to the fault size and the energy accumulated around it. Depending on the strength of an interaction, the toppling events exhibit temporal clustering as is ubiquitously observed for natural earthquakes. Defining a geometrical parameter that characterizes the heterogeneity of the energy stored in the nodes, we show that aftershocks are characterized as a process of regaining the heterogeneity that is lost by the main shock. The heterogeneity is not significantly altered during the loading process and foreshocks.
Dynamics of an epidemic model with quarantine on scale-free networks
Kang, Huiyan; Liu, Kaihui; Fu, Xinchu
2017-12-01
Quarantine strategies are frequently used to control or reduce the transmission risks of epidemic diseases such as SARS, tuberculosis and cholera. In this paper, we formulate a susceptible-exposed-infected-quarantined-recovered model on a scale-free network incorporating the births and deaths of individuals. Considering that the infectivity is related to the degrees of infectious nodes, we introduce quarantined rate as a function of degree into the model, and quantify the basic reproduction number, which is shown to be dependent on some parameters, such as quarantined rate, infectivity and network structures. A theoretical result further indicates the heterogeneity of networks and higher infectivity will raise the disease transmission risk while quarantine measure will contribute to the prevention of epidemic spreading. Meanwhile, the contact assumption between susceptibles and infectives may impact the disease transmission. Furthermore, we prove that the basic reproduction number serves as a threshold value for the global stability of the disease-free and endemic equilibria and the uniform persistence of the disease on the network by constructing appropriate Lyapunov functions. Finally, some numerical simulations are illustrated to perform and complement our analytical results.
Emergency response to disaster-struck scale-free network with redundant systems
Ouyang, Min; Yu, Ming-Hui; Huang, Xiang-Zhao; Luan, En-Jie
2008-07-01
Disasters cause tremendous damage every year. In this paper, we have specifically studied emergency response to disaster-struck scale-free networks when some nodes in the network have redundant systems. If one node collapses, its redundant system will substitute it to work for a period of time. In the first part, according to the network structure, several redundant strategies have been formulated, and then our studies focused on their effectiveness by means of simulation. Results show that the strategy based on total degrees is the most effective one. However, many nodes still collapse in the end if redundant systems do not have sufficient capability, so emergency responses are necessary. Several emergent strategies controlling the distribution of external resources have been proposed in the second part. The effectiveness of those emergent strategies are then studied from three aspects, such as the effect of strategies on spreading processes, minimum sufficient quantities of external resources and determination of the most appropriate emergent strategy. In addition, the effects of redundant intensity on these aspects have been discussed as well.
Extraversion is encoded by scale-free dynamics of default mode network.
Lei, Xu; Zhao, Zhiying; Chen, Hong
2013-07-01
Resting-state functional Magnetic Resonance Imaging (rsfMRI) is a powerful tool to investigate neurological and psychiatric diseases. Recently, the evidences linking the scaling properties of resting-state activity and the personality have been accumulated. However, it remains unknown whether the personality is associated with the scale-free dynamics of default mode network (DMN) - the most widely studied network in the rsfMRI literatures. To investigate this question, we estimated the Hurst exponent, quantifying long memory of a time-series, in DMN of rsfMRI in 20 healthy individuals. The Hurst exponent in DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with the extraversion score of the revised Eysenck Personality Questionnaire. Specifically, longer memory in DMN corresponded to lower extraversion. We provide evidences for an association between individual differences in personality and scaling dynamics in DMN, whose alteration has been previously linked with introspective cognition. This association might arise from the efficiency in online information processing. Our results suggest that personality trait may be reflected by the scaling property of resting-state networks. Copyright © 2012 Elsevier Inc. All rights reserved.
Modeling the spread of virus in packets on scale free network
Lamzabi, S.; Lazfi, S.; Rachadi, A.; Ez-Zahraouy, H.; Benyoussef, A.
2016-01-01
In this paper, we propose a new model for computer virus attacks and recovery at the level of information packets. The model we propose is based on one hand on the susceptible-infected (SI) and susceptible-infected-recovered (SIR) stochastic epidemic models for computer virus propagation and on the other hand on the time-discrete Markov chain of the minimal traffic routing protocol. We have applied this model to the scale free Barabasi-Albert network to determine how the dynamics of virus propagation is affected by the traffic flow in both the free-flow and the congested phases. The numerical results show essentially that the proportion of infected and recovered packets increases when the rate of infection λ and the recovery rate β increase in the free-flow phase while in the congested phase, the number of infected (recovered) packets presents a maximum (minimum) at certain critical value of β characterizing a certain competition between the infection and the recovery rates.
Modified Penna bit-string network evolution model for scale-free networks with assortative mixing
Kim, Yup; Choi, Woosik; Yook, Soon-Hyung
2012-02-01
Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.
Fast sparsely synchronized brain rhythms in a scale-free neural network
Kim, Sang-Yoon; Lim, Woochang
2015-08-01
We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D
Modeling interactome: scale-free or geometric?
Przulj, N; Corneil, D G; Jurisica, I
2004-12-12
Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have as accurate a model as possible. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. One example of large and complex networks involves protein-protein interaction (PPI) networks. We analyze PPI networks of yeast Saccharomyces cerevisiae and fruitfly Drosophila melanogaster using a newly introduced measure of local network structure as well as the standardly used measures of global network structure. We examine the fit of four different network models, including Erdos-Renyi, scale-free and geometric random network models, to these PPI networks with respect to the measures of local and global network structure. We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI data. We hypothesize that only the noise in these networks is scale-free. We systematically evaluate how well-different network models fit the PPI networks. We show that the structure of PPI networks is better modeled by a geometric random graph than by a scale-free model. Supplementary information is available at http://www.cs.utoronto.ca/~juris/data/data/ppiGRG04/
Xie, Fengjie; Shi, Jing; Lin, Jun
2017-01-01
In this work, we study an evolutionary prisoner's dilemma game (PDG) on Barabási-Albert scale-free networks with limited player interactions, and explore the effect of interaction style and degree on cooperation. The results show that high-degree preference interaction, namely the most applicable interaction in the real world, is less beneficial for emergence of cooperation on scale-free networks than random interaction. Besides, cooperation on scale-free networks is enhanced with the increase of interaction degree regardless whether the interaction is high-degree preference or random. If the interaction degree is very low, the cooperation level on scale-free networks is much lower than that on regular ring networks, which is against the common belief that scale-free networks must be more beneficial for cooperation. Our analysis indicates that the interaction relations, the strategy and the game payoff of high-connectivity players play important roles in the evolution of cooperation on scale-free networks. A certain number of interactions are necessary for scale-free networks to exhibit strong capability of facilitating cooperation. Our work provides important insight for members on how to interact with others in a social organization.
Directory of Open Access Journals (Sweden)
Fengjie Xie
Full Text Available In this work, we study an evolutionary prisoner's dilemma game (PDG on Barabási-Albert scale-free networks with limited player interactions, and explore the effect of interaction style and degree on cooperation. The results show that high-degree preference interaction, namely the most applicable interaction in the real world, is less beneficial for emergence of cooperation on scale-free networks than random interaction. Besides, cooperation on scale-free networks is enhanced with the increase of interaction degree regardless whether the interaction is high-degree preference or random. If the interaction degree is very low, the cooperation level on scale-free networks is much lower than that on regular ring networks, which is against the common belief that scale-free networks must be more beneficial for cooperation. Our analysis indicates that the interaction relations, the strategy and the game payoff of high-connectivity players play important roles in the evolution of cooperation on scale-free networks. A certain number of interactions are necessary for scale-free networks to exhibit strong capability of facilitating cooperation. Our work provides important insight for members on how to interact with others in a social organization.
Li, Wei; Zhang, Xiaoming; Hu, Gang
2007-10-01
We study how heterogeneous degree distributions and large-scale collective cooperation in social networks emerge in complex homogeneous systems by a simple local rule: learning from the best in both strategy selections and linking choices. The prisoner's dilemma game is used as the local dynamics. We show that the social structure may evolve into single-scale, broad-scale, and scale-free (SF) degree distributions for different control parameters. In particular, in a relatively strong-selfish parameter region the SF property can be self-organized in social networks by dynamic evolutions and these SF structures help the whole node community to reach a high level of cooperation under the poor condition of a high selfish intention of individuals.
Correlations and clustering in a scale-free network in Euclidean space
Indian Academy of Sciences (India)
Internet has with other networks is that a large part of Internet is embedded in. Euclidean space by the .... network, it always gets a connection to the existing local nodes of the Internet net- work. In fact one would ..... [6] R Pastor-Satorras and A Vespigniani, Evolution and structure of internet: A statis- tical physics approach ...
Efficient path routing strategy for flows with multiple priorities on scale-free networks.
Zhang, Xi; Zhou, Zhili; Cheng, Dong
2017-01-01
In real networks, traffic flows are different in amount as well as their priorities. However, the latter priority has rarely been examined in routing strategy studies. In this paper, a novel routing algorithm, which is based on the efficient path routing strategy (EP), is proposed to overcome network congestion problem caused by large amount of traffic flows with different priorities. In this scheme, traffic flows with different priorities are transmitted through different routing paths, which are based on EP with different parameters. Simulation results show that the traffic capacity for flows with different priorities can be enhanced by 12% with this method, compared with EP. In addition, the new method contributes to more balanced network traffic load distribution and reduces average transmission jump and delay of packets.
Directory of Open Access Journals (Sweden)
Jacob W Malcom
Full Text Available One of the goals of biology is to bridge levels of organization. Recent technological advances are enabling us to span from genetic sequence to traits, and then from traits to ecological dynamics. The quantitative genetics parameter heritability describes how quickly a trait can evolve, and in turn describes how quickly a population can recover from an environmental change. Here I propose that we can link the details of the genetic architecture of a quantitative trait--i.e., the number of underlying genes and their relationships in a network--to population recovery rates by way of heritability. I test this hypothesis using a set of agent-based models in which individuals possess one of two network topologies or a linear genotype-phenotype map, 16-256 genes underlying the trait, and a variety of mutation and recombination rates and degrees of environmental change. I find that the network architectures introduce extensive directional epistasis that systematically hides and reveals additive genetic variance and affects heritability: network size, topology, and recombination explain 81% of the variance in average heritability in a stable environment. Network size and topology, the width of the fitness function, pre-change additive variance, and certain interactions account for ∼75% of the variance in population recovery times after a sudden environmental change. These results suggest that not only the amount of additive variance, but importantly the number of loci across which it is distributed, is important in regulating the rate at which a trait can evolve and populations can recover. Taken in conjunction with previous research focused on differences in degree of network connectivity, these results provide a set of theoretical expectations and testable hypotheses for biologists working to span levels of organization from the genotype to the phenotype, and from the phenotype to the environment.
Emergence of scale-free close-knit friendship structure in online social networks.
Directory of Open Access Journals (Sweden)
Ai-Xiang Cui
Full Text Available Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four
Stability of an SAIRS alcoholism model on scale-free networks
Xiang, Hong; Liu, Ying-Ping; Huo, Hai-Feng
2017-05-01
A new SAIRS alcoholism model with birth and death on complex heterogeneous networks is proposed. The total population of our model is partitioned into four compartments: the susceptible individual, the light problem alcoholic, the heavy problem alcoholic and the recovered individual. The spread of alcoholism threshold R0 is calculated by the next generation matrix method. When R0 alcohol free equilibrium is globally asymptotically stable, then the alcoholics will disappear. When R0 > 1, the alcoholism equilibrium is global attractivity, then the number of alcoholics will remain stable and alcoholism will become endemic. Furthermore, the modified SAIRS alcoholism model on weighted contact network is introduced. Dynamical behavior of the modified model is also studied. Numerical simulations are also presented to verify and extend theoretical results. Our results show that it is very important to treat alcoholics to control the spread of the alcoholism.
Dynamic source routing strategy for two-level flows on scale-free networks.
Jiang, Zhong-Yuan; Liang, Man-Gui; Wu, Jia-Jing
2013-01-01
Packets transmitting in real communication networks such as the Internet can be classified as time-sensitive or time-insensitive. To better support the real-time and time-insensitive applications, we propose a two-level flow traffic model in which packets are labeled as level-1 or level-2, and those with level-1 have higher priority to be transmitted. In order to enhance the traffic capacity of the two-level flow traffic model, we expand the global dynamic routing strategy and propose a new dynamic source routing which supports no routing-flaps, high traffic capacity, and diverse traffic flows. As shown in this paper, the proposed dynamic source routing can significantly enhance the traffic capacity and quality of time-sensitive applications compared with the global shortest path routing strategy.
Energy Technology Data Exchange (ETDEWEB)
Perumalla, Kalyan S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Alam, Maksudul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2017-10-01
A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.
Ma, Fei; Yao, Bing
2017-10-01
It is always an open, demanding and difficult task for generating available model to simulate dynamical functions and reveal inner principles from complex systems and networks. In this article, due to lots of real-life and artificial networks are built from series of simple and small groups (components), we discuss some interesting and helpful network-operation to generate more realistic network models. In view of community structure (modular topology), we present a class of sparse network models N(t , m) . At the moment, we capture the fact the N(t , 4) has not only scale-free feature, which means that the probability that a randomly selected vertex with degree k decays as a power-law, following P(k) ∼k-γ, where γ is the degree exponent, but also small-world property, which indicates that the typical distance between two uniform randomly chosen vertices grows proportionally to logarithm of the order of N(t , 4) , namely, relatively shorter diameter and lower average path length, simultaneously displays higher clustering coefficient. Next, as a new topological parameter correlating to reliability, synchronization capability and diffusion properties of networks, the number of spanning trees over a network is studied in more detail, an exact analytical solution for the number of spanning trees of the N(t , 4) is obtained. Based on the network-operation, part hub-vertex linking with each other will be helpful for structuring various network models and investigating the rules related with real-life networks.
Berker, A Nihat; Hinczewski, Michael; Netz, Roland R
2009-10-01
Percolation in a scale-free hierarchical network is solved exactly by renormalization-group theory in terms of the different probabilities of short-range and long-range bonds. A phase of critical percolation, with algebraic [Berezinskii-Kosterlitz-Thouless (BKT)] geometric order, occurs in the phase diagram in addition to the ordinary (compact) percolating phase and the nonpercolating phase. It is found that no connection exists between, on the one hand, the onset of this geometric BKT behavior and, on the other hand, the onsets of the highly clustered small-world character of the network and of the thermal BKT transition of the Ising model on this network. Nevertheless, both geometric and thermal BKT behaviors have inverted characters, occurring where disorder is expected, namely, at low bond probability and high temperature, respectively. This may be a general property of long-range networks.
Directory of Open Access Journals (Sweden)
McGhee Timothy
2009-02-01
Full Text Available Abstract Background While standard reductionist approaches have provided some insights into specific gene polymorphisms and molecular pathways involved in disease pathogenesis, our understanding of complex traits such as atherosclerosis or type 2 diabetes remains incomplete. Gene expression profiling provides an unprecedented opportunity to understand complex human diseases by providing a global view of the multiple interactions across the genome that are likely to contribute to disease pathogenesis. Thus, the goal of gene expression profiling is not to generate lists of differentially expressed genes, but to identify the physiologic or pathogenic processes and structures represented in the expression profile. Methods RNA was separately extracted from peripheral blood neutrophils and mononuclear leukocytes, labeled, and hybridized to genome level microarrays to generate expression profiles of children with polyarticular juvenile idiopathic arthritis, juvenile dermatomyositis relative to childhood controls. Statistically significantly differentially expressed genes were identified from samples of each disease relative to controls. Functional network analysis identified interactions between products of these differentially expressed genes. Results In silico models of both diseases demonstrated similar features with properties of scale-free networks previously described in physiologic systems. These networks were observable in both cells of the innate immune system (neutrophils and cells of the adaptive immune system (peripheral blood mononuclear cells. Conclusion Genome-level transcriptional profiling from childhood onset rheumatic diseases suggested complex interactions in two arms of the immune system in both diseases. The disease associated networks showed scale-free network patterns similar to those reported in normal physiology. We postulate that these features have important implications for therapy as such networks are relatively resistant
Wang, Shuai; Liu, Jing
2016-07-01
The emergence of cooperation is one of the key metaphors behind many social disciplines and phenomena. Existing studies show that intentional attacks on nodes damage the robustness of cooperation distinctly, and the heterogeneity among nodes promotes the emergence of cooperation. However, the impact of link-based attacks and the connectivity of networks on the cooperative mechanism is still unclear. In this paper, we focus on the effect of edge removal on the emergence of cooperation together with the connectivity of networks. The results show that malicious attacks evaluated by edge-degree are efficient to invade cooperators, and heterogeneous networks tend to perform poorly when attacks arise. Furthermore, we analyze the performance of several recovering strategies, and conclude that the connectivity is crucial to the recovery of functionality of whole networks.
The impact of neighboring infection on the computer virus spread in packets on scale-free networks
Lazfi, S.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.
2017-12-01
In this paper, we introduce the effect of neighbors on the infection of packets by computer virus in the SI and SIR models using the minimal traffic routing protocol. We have applied this model to the Barabasi-Albert network to determine how intrasite and extrasite infection rates affect virus propagation through the traffic flow of information packets in both the free-flow and the congested phases. The numerical results show that when we change the intrasite infection rate λ1 while keeping constant the extrasite infection rate λ2, we get normal behavior in the congested phase: in the network, the proportion of infected packets increases to reach a peak and then decreases resulting in a simultaneous increase of the recovered packets. In contrast, when the intrasite infection rate λ1 is kept fixed, an increase of the extrasite infection rate results in two regimes: The first one is characterized by an increase of the proportion of infected packets until reaching some peak value and then decreases smoothly. The second regime is characterized by an increase of infected packets to some stationary value.
Moisset de Espanés, P; Osses, A; Rapaport, I
2016-12-01
Fixed points are fundamental states in any dynamical system. In the case of gene regulatory networks (GRNs) they correspond to stable genes profiles associated to the various cell types. We use Kauffman's approach to model GRNs with random Boolean networks (RBNs). In this paper we explore how the topology affects the distribution of the number of fixed points in randomly generated networks. We also study the size of the basins of attraction of these fixed points if we assume the α-asynchronous dynamics (where every node is updated independently with probability 0≤α≤1). It is well-known that asynchrony avoids the cyclic attractors into which parallel dynamics tends to fall. We observe the remarkable property that, in all our simulations, if for a given RBN with Barabási-Albert topology and α-asynchronous dynamics an initial configuration reaches a fixed point, then every configuration also reaches a fixed point. By contrast, in the parallel regime, the percentage of initial configurations reaching a fixed point (for the same networks) is dramatically smaller. We contrast the results of the simulations on Barabási-Albert networks with the classical Erdös-Rényi model of random networks. Everything indicates that Barabási-Albert networks are extremely robust. Finally, we study the mean and maximum time/work needed to reach a fixed point when starting from randomly chosen initial configurations. Copyright Â© 2016 Elsevier Ireland Ltd. All rights reserved.
Ma, Fei; Su, Jing; Hao, Yongxing; Yao, Bing; Yan, Guanghui
2018-02-01
The problem of uncovering the internal operating function of network models is intriguing, demanded and attractive in researches of complex networks. Notice that, in the past two decades, a great number of artificial models are built to try to answer the above mentioned task. Based on the different growth ways, these previous models can be divided into two categories, one type, possessing the preferential attachment, follows a power-law P(k) ∼k-γ, 2 elements.
2013-02-22
... Employment and Training Administration Goodman Networks, Inc., Core Network Engineering (Deployment Engineering) Division Including Workers in the Core Network Engineering (Deployment Engineering) Division in... of Goodman Networks, Inc., Core Network Engineering (Deployment Engineering) Division, including...
Scale-free music of the brain.
Wu, Dan; Li, Chao-Yi; Yao, De-Zhong
2009-06-15
There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG. In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, Pbrain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.
Scale-free music of the brain.
Directory of Open Access Journals (Sweden)
Dan Wu
Full Text Available BACKGROUND: There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG. METHODOLOGY/PRINCIPAL FINDINGS: In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM and slow-wave sleep (SWS. The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN, 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P<0.001. We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners. CONCLUSIONS/SIGNIFICANCE: The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.
Wikipedia information flow analysis reveals the scale-free architecture of the semantic space.
Directory of Open Access Journals (Sweden)
Adolfo Paolo Masucci
Full Text Available In this paper we extract the topology of the semantic space in its encyclopedic acception, measuring the semantic flow between the different entries of the largest modern encyclopedia, Wikipedia, and thus creating a directed complex network of semantic flows. Notably at the percolation threshold the semantic space is characterised by scale-free behaviour at different levels of complexity and this relates the semantic space to a wide range of biological, social and linguistics phenomena. In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free. Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties. However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process. After giving a detailed description and interpretation of the topological properties of the semantic space, we introduce a stochastic model of content-based network, based on a copy and mutation algorithm and on the Heaps' law, that is able to capture the main statistical properties of the analysed semantic space, including the Zipf's law for the word frequency distribution.
Wikipedia information flow analysis reveals the scale-free architecture of the semantic space.
Masucci, Adolfo Paolo; Kalampokis, Alkiviadis; Eguíluz, Victor Martínez; Hernández-García, Emilio
2011-02-28
In this paper we extract the topology of the semantic space in its encyclopedic acception, measuring the semantic flow between the different entries of the largest modern encyclopedia, Wikipedia, and thus creating a directed complex network of semantic flows. Notably at the percolation threshold the semantic space is characterised by scale-free behaviour at different levels of complexity and this relates the semantic space to a wide range of biological, social and linguistics phenomena. In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free. Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties. However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process. After giving a detailed description and interpretation of the topological properties of the semantic space, we introduce a stochastic model of content-based network, based on a copy and mutation algorithm and on the Heaps' law, that is able to capture the main statistical properties of the analysed semantic space, including the Zipf's law for the word frequency distribution.
Entanglement percolation on a quantum internet with scale-free and clustering characters
Wu, Liang; Zhu, Shiqun
2011-11-01
The applicability of entanglement percolation protocol to real Internet structure is investigated. If the current Internet can be used directly in the quantum regime, the protocol can provide a way to establish long-distance entanglement when the links are pure nonmaximally entangled states. This applicability is primarily due to the combination of scale-free degree distribution and a high level of clustering, both of which are widely observed in many natural and artificial networks including the current Internet. It suggests that the topology of real Internet may play an important role in entanglement establishment.
The emergence of overlapping scale-free genetic architecture in digital organisms.
Gerlee, P; Lundh, T
2008-01-01
We have studied the evolution of genetic architecture in digital organisms and found that the gene overlap follows a scale-free distribution, which is commonly found in metabolic networks of many organisms. Our results show that the slope of the scale-free distribution depends on the mutation rate and that the gene development is driven by expansion of already existing genes, which is in direct correspondence to the preferential growth algorithm that gives rise to scale-free networks. To further validate our results we have constructed a simple model of gene development, which recapitulates the results from the evolutionary process and shows that the mutation rate affects the tendency of genes to cluster. In addition we could relate the slope of the scale-free distribution to the genetic complexity of the organisms and show that a high mutation rate gives rise to a more complex genetic architecture.
The temporal structures and functional significance of scale-free brain activity.
He, Biyu J; Zempel, John M; Snyder, Abraham Z; Raichle, Marcus E
2010-05-13
Scale-free dynamics, with a power spectrum following P proportional to f(-beta), are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. The functional significance of scale-free brain activity is indicated by task performance modulation and regional variation, with beta being larger in default network and visual cortex and smaller in hippocampus and cerebellum. The precise patterns of nested frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic waves and stock market fluctuations, suggesting system-specific generative mechanisms. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications. Copyright 2010 Elsevier Inc. All rights reserved.
The Vulnerability of Some Networks including Cycles via Domination Parameters
Tufan Turaci; Hüseyin Aksan
2016-01-01
Let G=(V(G),E(G)) be an undirected simple connected graph. A network is usually represented by an undirected simple graph where vertices represent processors and edges represent links between processors. Finding the vulnerability values of communication networks modeled by graphs is important for network designers. The vulnerability value of a communication network shows the resistance of the network after the disruption of some centers or connection lines until a communication breakdown. The...
Scale-free systems organization as entropy competition
Sanchirico, A.; Fiorentino, M.
2009-04-01
networks, technological systems, as electronic circuits, geomorphological systems, as river networks, and so on. Here, based on statistical mechanics, we discuss how network systems organize themselves into an equilibrium scale-free structure. In particular, we show that the power-law is the most probable distribution that both nodes and edges, in a reciprocal competition, assume when the respective entropy functions reach their maxima, under mutual constraint. The proposed approach predicts scaling exponent values in agreement with those most frequently observed in nature.
The Vulnerability of Some Networks including Cycles via Domination Parameters
Directory of Open Access Journals (Sweden)
Tufan Turaci
2016-01-01
Full Text Available Let G=(V(G,E(G be an undirected simple connected graph. A network is usually represented by an undirected simple graph where vertices represent processors and edges represent links between processors. Finding the vulnerability values of communication networks modeled by graphs is important for network designers. The vulnerability value of a communication network shows the resistance of the network after the disruption of some centers or connection lines until a communication breakdown. The domination number and its variations are the most important vulnerability parameters for network vulnerability. Some variations of domination numbers are the 2-domination number, the bondage number, the reinforcement number, the average lower domination number, the average lower 2-domination number, and so forth. In this paper, we study the vulnerability of cycles and related graphs, namely, fans, k-pyramids, and n-gon books, via domination parameters. Then, exact solutions of the domination parameters are obtained for the above-mentioned graphs.
An architecture including network QoS in scientific workflows
Zhao, Z.; Grosso, P.; Koning, R.; van der Ham, J.; de Laat, C.
2010-01-01
The quality of the network services has so far rarely been considered in composing and executing scientific workflows. Currently, scientific applications tune the execution quality of workflows neglecting network resources, and by selecting only optimal software services and computing resources. One
Improving Estimation of Betweenness Centrality for Scale-Free Graphs
Energy Technology Data Exchange (ETDEWEB)
Bromberger, Seth A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Klymko, Christine F. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henderson, Keith A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pearce, Roger [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoff [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-11-07
Betweenness centrality is a graph statistic used to nd vertices that are participants in a large number of shortest paths in a graph. This centrality measure is commonly used in path and network interdiction problems and its complete form requires the calculation of all-pairs shortest paths for each vertex. This leads to a time complexity of O(jV jjEj), which is impractical for large graphs. Estimation of betweenness centrality has focused on performing shortest-path calculations on a subset of randomly- selected vertices. This reduces the complexity of the centrality estimation to O(jSjjEj); jSj < jV j, which can be scaled appropriately based on the computing resources available. An estimation strategy that uses random selection of vertices for seed selection is fast and simple to implement, but may not provide optimal estimation of betweenness centrality when the number of samples is constrained. Our experimentation has identi ed a number of alternate seed-selection strategies that provide lower error than random selection in common scale-free graphs. These strategies are discussed and experimental results are presented.
EEG microstate sequences in healthy humans at rest reveal scale-free dynamics
Van De Ville, Dimitri; Britz, Juliane; Michel, Christoph M.
2010-01-01
Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the “atoms of thoughts,” the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain. PMID:20921381
Network optimization including gas lift and network parameters under subsurface uncertainty
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)
2013-08-01
Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A
Scale-free transport in fusion plasmas: theory and applications
Energy Technology Data Exchange (ETDEWEB)
Sanchez, Raul [ORNL; Mier, Jose Angel [Universidad Carlos III, Madrid, Spain; Newman, David E [University of Alaska; Carreras, Benjamin A [BACV Solutions, Inc., Oak Ridge; Garcia, Luis [Universidad Carlos III, Madrid, Spain; Leboeuf, Jean-Noel [JNL Scientific, Inc., Casa Grande, AZ; Decyk, Viktor [University of California, Los Angeles
2008-01-01
A novel approach to detect the existence of scale-free transport in turbulent flows, based on the characterization of its Lagrangian characteristics, is presented and applied to two situations relevant for tokamak plasmas. The first one, radial transport in the presence of near-critical turbulence, has been known for quite some time to yield scale-free, superdiffusive transport. We use it to test the method and illustrate its robustness with respect to other approaches. The second situation, radial transport across radially-sheared poloidal zonal flows driven by turbulence via the Reynold stresses, is examined for the first time in this manner. The result is rather surprising and different from the traditionally assumed diffusive behavior. Instead, radial transport behaves instead in a scale-free, subdiffusive manner, which may have implications for the modeling of transport across transport barriers.
Scale-free random graphs and Potts model
Indian Academy of Sciences (India)
We introduce a simple algorithm that constructs scale-free random graphs efficiently: each vertex has a prescribed weight − (0 < < 1) and an edge can connect vertices and with rate . Corresponding equilibrium ensemble is identified and the problem is solved by the → 1 limit of the -state Potts ...
The scale-free dynamics of eukaryotic cells.
Directory of Open Access Journals (Sweden)
Miguel A Aon
Full Text Available Temporal organization of biological processes requires massively parallel processing on a synchronized time-base. We analyzed time-series data obtained from the bioenergetic oscillatory outputs of Saccharomyces cerevisiae and isolated cardiomyocytes utilizing Relative Dispersional (RDA and Power Spectral (PSA analyses. These analyses revealed broad frequency distributions and evidence for long-term memory in the observed dynamics. Moreover RDA and PSA showed that the bioenergetic dynamics in both systems show fractal scaling over at least 3 orders of magnitude, and that this scaling obeys an inverse power law. Therefore we conclude that in S. cerevisiae and cardiomyocytes the dynamics are scale-free in vivo. Applying RDA and PSA to data generated from an in silico model of mitochondrial function indicated that in yeast and cardiomyocytes the underlying mechanisms regulating the scale-free behavior are similar. We validated this finding in vivo using single cells, and attenuating the activity of the mitochondrial inner membrane anion channel with 4-chlorodiazepam to show that the oscillation of NAD(PH and reactive oxygen species (ROS can be abated in these two evolutionarily distant species. Taken together these data strongly support our hypothesis that the generation of ROS, coupled to redox cycling, driven by cytoplasmic and mitochondrial processes, are at the core of the observed rhythmicity and scale-free dynamics. We argue that the operation of scale-free bioenergetic dynamics plays a fundamental role to integrate cellular function, while providing a framework for robust, yet flexible, responses to the environment.
Faster Parallel Traversal of Scale Free Graphs at Extreme Scale with Vertex Delegates
Pearce, Roger
2014-11-01
© 2014 IEEE. At extreme scale, irregularities in the structure of scale-free graphs such as social network graphs limit our ability to analyze these important and growing datasets. A key challenge is the presence of high-degree vertices (hubs), that leads to parallel workload and storage imbalances. The imbalances occur because existing partitioning techniques are not able to effectively partition high-degree vertices. We present techniques to distribute storage, computation, and communication of hubs for extreme scale graphs in distributed memory supercomputers. To balance the hub processing workload, we distribute hub data structures and related computation among a set of delegates. The delegates coordinate using highly optimized, yet portable, asynchronous broadcast and reduction operations. We demonstrate scalability of our new algorithmic technique using Breadth-First Search (BFS), Single Source Shortest Path (SSSP), K-Core Decomposition, and Page-Rank on synthetically generated scale-free graphs. Our results show excellent scalability on large scale-free graphs up to 131K cores of the IBM BG/P, and outperform the best known Graph500 performance on BG/P Intrepid by 15%
Strategic Factor Markets Scale Free Resources and Economic Performance
DEFF Research Database (Denmark)
Geisler Asmussen, Christian
2015-01-01
This paper analyzes how scale free resources, which can be acquired by multiple firms simultaneously and deployed against one another in product market competition, will be priced in strategic factor markets, and what the consequences are for the acquiring firms' performance. Based on a game...... at (and largely succeed in) setting resource prices so that the acquiring firms earn negative strategic factor market profits—sacrificing some of their preexisting market power rents—by acquiring resources that they know to be overpriced....
Percolation in Self-Similar Networks
Serrano, M. Ángeles; Krioukov, Dmitri; Boguñá, Marián
2011-01-01
We provide a simple proof that graphs in a general class of self-similar networks have zero percolation threshold. The considered self-similar networks include random scale-free graphs with given expected node degrees and zero clustering, scale-free graphs with finite clustering and metric structure, growing scale-free networks, and many real networks. The proof and the derivation of the giant component size do not require the assumption that networks are treelike. Our results rely only on the observation that self-similar networks possess a hierarchy of nested subgraphs whose average degree grows with their depth in the hierarchy. We conjecture that this property is pivotal for percolation in networks.
On thermodynamic states of the Ising model on scale-free graphs
Directory of Open Access Journals (Sweden)
Yu. Kozitsky
2013-06-01
Full Text Available There is proposed a model of scale-free random graphs which are locally close to the uncorrelated complex random networks with divergent 2> studied in, e.g., S. N. Dorogovtsev et al, Rev. Mod. Phys., 80, 1275 (2008. It is shown that the Ising model on the proposed graphs with interaction intensities of arbitrary signs with probability one is in a paramagnetic state at sufficiently high finite values of the temperature. For the same graphs, the bond percolation model with probability one is in a nonpercolative state for positive values of the percolation probability. These results and their possible extensions are also discussed.
Emergence of scale-free leadership structure in social recommender systems.
Zhou, Tao; Medo, Matúš; Cimini, Giulio; Zhang, Zi-Ke; Zhang, Yi-Cheng
2011-01-01
The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems.
The analysis of HIV/AIDS drug-resistant on networks
Liu, Maoxing
2014-01-01
In this paper, we present an Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS) drug-resistant model using an ordinary differential equation (ODE) model on scale-free networks. We derive the threshold for the epidemic to be zero in infinite scale-free network. We also prove the stability of disease-free equilibrium (DFE) and persistence of HIV/AIDS infection. The effects of two immunization schemes, including proportional scheme and targeted vaccination, are studied and compared. We find that targeted strategy compare favorably to a proportional condom using has prominent effect to control HIV/AIDS spread on scale-free networks.
Generate the scale-free brain music from BOLD signals.
Lu, Jing; Guo, Sijia; Chen, Mingming; Wang, Weixia; Yang, Hua; Guo, Daqing; Yao, Dezhong
2018-01-01
Many methods have been developed to translate a human electroencephalogram (EEG) into music. In addition to EEG, functional magnetic resonance imaging (fMRI) is another method used to study the brain and can reflect physiological processes. In 2012, we established a method to use simultaneously recorded fMRI and EEG signals to produce EEG-fMRI music, which represents a step toward scale-free brain music. In this study, we used a neural mass model, the Jansen-Rit model, to simulate activity in several cortical brain regions. The interactions between different brain regions were represented by the average normalized diffusion tensor imaging (DTI) structural connectivity with a coupling coefficient that modulated the coupling strength. Seventy-eight brain regions were adopted from the Automated Anatomical Labeling (AAL) template. Furthermore, we used the Balloon-Windkessel hemodynamic model to transform neural activity into a blood-oxygen-level dependent (BOLD) signal. Because the fMRI BOLD signal changes slowly, we used a sampling rate of 250 Hz to produce the temporal series for music generation. Then, the BOLD music was generated for each region using these simulated BOLD signals. Because the BOLD signal is scale free, these music pieces were also scale free, which is similar to classic music. Here, to simulate the case of an epileptic patient, we changed the parameter that determined the amplitude of the excitatory postsynaptic potential (EPSP) in the neural mass model. Finally, we obtained BOLD music for healthy and epileptic patients. The differences in levels of arousal between the 2 pieces of music may provide a potential tool for discriminating the different populations if the differences can be confirmed by more real data. Copyright © 2017 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.
Scale-free texture of the fast solar wind
Hnat, B.; Chapman, S. C.; Gogoberidze, G.; Wicks, R. T.
2011-12-01
The higher-order statistics of magnetic field magnitude fluctuations in the fast quiet solar wind are quantified systematically, scale by scale. We find a single global non-Gaussian scale-free behavior from minutes to over 5 h. This spans the signature of an inertial range of magnetohydrodynamic turbulence and a ˜1/f range in magnetic field components. This global scaling in field magnitude fluctuations is an intrinsic component of the underlying texture of the solar wind and puts a strong constraint on any theory of solar corona and the heliosphere. Intriguingly, the magnetic field and velocity components show scale-dependent dynamic alignment outside of the inertial range.
Scale-free dynamics of somatic adaptability in immune system
Saito, Shiro
2009-01-01
The long-time dynamics of somatic adaptability in immune system is simulated by a simple physical model. The immune system described by the model exhibits a scale free behavior as is observed in living systems. The balance between the positive and negative feedbacks of the model leads to a robust immune system where the positive one corresponds to the formation of memory cells and the negative one to immunosuppression. Also the immunosenescence of the system is discussed based on the time-dependence of the epigenetic landscape of the adaptive immune cells in the shape space.
2011-04-28
... they begin to deploy Smart Grid. Hospitals and healthcare providers can leverage broadband technologies... COMMISSION Reliability and Continuity of Communications Networks, Including Broadband Technologies; Effects... broadband technologies. 4. Today's increasingly interconnected world is one in which communications services...
Community structure and scale-free collections of Erdős-Rényi graphs.
Seshadhri, C; Kolda, Tamara G; Pinar, Ali
2012-05-01
Community structure plays a significant role in the analysis of social networks and similar graphs, yet this structure is little understood and not well captured by most models. We formally define a community to be a subgraph that is internally highly connected and has no deeper substructure. We use tools of combinatorics to show that any such community must contain a dense Erdős-Rényi (ER) subgraph. Based on mathematical arguments, we hypothesize that any graph with a heavy-tailed degree distribution and community structure must contain a scale-free collection of dense ER subgraphs. These theoretical observations corroborate well with empirical evidence. From this, we propose the Block Two-Level Erdős-Rényi (BTER) model, and demonstrate that it accurately captures the observable properties of many real-world social networks.
Design and Optimization of Capacitated Supply Chain Networks Including Quality Measures
Directory of Open Access Journals (Sweden)
Krystel K. Castillo-Villar
2014-01-01
Full Text Available This paper presents (1 a novel capacitated model for supply chain network design which considers manufacturing, distribution, and quality costs (named SCND-COQ model and (2 five combinatorial optimization methods, based on nonlinear optimization, heuristic, and metaheuristic approaches, which are used to solve realistic instances of practical size. The SCND-COQ model is a mixed-integer nonlinear problem which can be used at a strategic planning level to design a supply chain network that maximizes the total profit subject to meeting an overall quality level of the final product at minimum costs. The SCND-COQ model computes the quality-related costs for the whole supply chain network considering the interdependencies among business entities. The effectiveness of the proposed solution approaches is shown using numerical experiments. These methods allow solving more realistic (capacitated supply chain network design problems including quality-related costs (inspections, rework, opportunity costs, and others within a reasonable computational time.
Lipiński, Michał; Rydzewska, Grażyna; Foltyn, Wanda; Andrysiak-Mamos, Elżbieta; Bałdys-Waligórska, Agata; Bednarczuk, Tomasz; Blicharz-Dorniak, Jolanta; Bolanowski, Marek; Boratyn-Nowicka, Agnieszka; Borowska, Małgorzata; Cichocki, Andrzej; Ćwikła, Jarosław B; Falconi, Massimo; Handkiewicz-Junak, Daria; Hubalewska-Dydejczyk, Alicja; Jarząb, Barbara; Junik, Roman; Kajdaniuk, Dariusz; Kamiński, Grzegorz; Kolasińska-Ćwikła, Agnieszka; Kowalska, Aldona; Król, Robert; Królicki, Leszek; Kunikowska, Jolanta; Kuśnierz, Katarzyna; Lampe, Paweł; Lange, Dariusz; Lewczuk-Myślicka, Anna; Lewiński, Andrzej; Londzin-Olesik, Magdalena; Marek, Bogdan; Nasierowska-Guttmejer, Anna; Nowakowska-Duława, Ewa; Pilch-Kowalczyk, Joanna; Poczkaj, Karolina; Rosiek, Violetta; Ruchała, Marek; Siemińska, Lucyna; Sowa-Staszczak, Anna; Starzyńska, Teresa; Steinhof-Radwańska, Katarzyna; Strzelczyk, Janusz; Sworczak, Krzysztof; Syrenicz, Anhelli; Szawłowski, Andrzej; Szczepkowski, Marek; Wachuła, Ewa; Zajęcki, Wojciech; Zemczak, Anna; Zgliczyński, Wojciech; Kos-Kudła, Beata
2017-01-01
This paper presents the updated Polish Neuroendocrine Tumour Network expert panel recommendations on the management of neuroendocrine neoplasms (NENs) of the stomach and duodenum, including gastrinoma. The recommendations discuss the epidemiology, pathogenesis, and clinical presentation of these tumours as well as their diagnosis, including biochemical, histopathological, and localisation diagnoses. The principles of treatment are discussed, including endoscopic, surgical, pharmacological, and radionuclide treatments. Finally, there are also recommendations on patient monitoring.
Scale-Free Relationships between Social and Landscape Factors in Urban Systems
Directory of Open Access Journals (Sweden)
Chunzhu Wei
2017-01-01
Full Text Available Urban planners and ecologists have long debated the relationship between the structure of urban landscapes and social activities. There have, however, been very few discussions as to whether any such relationships might depend on the scales of observation. This work applies a hierarchical zoning technique to data from the city of Quito, Ecuador, to examine how relationships between typical spatial landscape metrics and social indicators depend on zoning scales. Our results showed that the estimates of both landscape heterogeneity features and social indicators significantly depend on the zoning scale. The mean values of the typical landscape metrics and the social indicators all exhibited predictable responses to a changing zoning scale, suggesting a consistent and significant scaling relationship within the multiple zoning scales. Yet relationships between these pairs of variables remain notably invariant to scale. This quantitative demonstration of the scale-free nature of the relationship between landscape characteristics and social indicators furthers our understanding of the relationships between landscape structures and social aspects of urban spaces, including deprivation and public service accessibility. The relationships between social indicators and one typical landscape aggregation metric (represented as the percentage of like adjacencies were nevertheless significantly dependent on scale, suggesting the importance of zoning scale decisions for analyzing the relationships between the social indicators and the landscape characteristics related with landscape adjacency. Aside from this typical landscape aggregation metric, the general invariance to the zoning scale of relationships between landscape structures and socioeconomic indicators in Quito suggests the importance of applying these scale-free relationships in understanding complex socio-ecological systems in other cities, which are shaped by the conflated influences of both
Quartic chameleons: Safely scale-free in the early Universe
Miller, Carisa; Erickcek, Adrienne L.
2016-11-01
In chameleon gravity, there exists a light scalar field that couples to the trace of the stress-energy tensor in such a way that its mass depends on the ambient matter density, and the field is screened in local, high-density environments. Recently it was shown that, for the runaway potentials commonly considered in chameleon theories, the field's coupling to matter and the hierarchy of scales between Standard Model particles and the energy scale of such potentials result in catastrophic effects in the early Universe when these particles become nonrelativistic. Perturbations with trans-Planckian energies are excited, and the theory suffers a breakdown in calculability at the relatively low temperatures of big bang nucleosynthesis. We consider a chameleon field in a quartic potential and show that the scale-free nature of this potential allows the chameleon to avoid many of the problems encountered by runaway potentials. Following inflation, the chameleon field oscillates around the minimum of its effective potential, and rapid changes in its effective mass excite perturbations via quantum particle production. The quartic model, however, only generates high-energy perturbations at comparably high temperatures and is able remain a well-behaved effective field theory at nucleosynthesis.
Mechanical failure in amorphous solids: Scale-free spinodal criticality
Procaccia, Itamar; Rainone, Corrado; Singh, Murari
2017-09-01
The mechanical failure of amorphous media is a ubiquitous phenomenon from material engineering to geology. It has been noticed for a long time that the phenomenon is "scale-free," indicating some type of criticality. In spite of attempts to invoke "Self-Organized Criticality," the physical origin of this criticality, and also its universal nature, being quite insensitive to the nature of microscopic interactions, remained elusive. Recently we proposed that the precise nature of this critical behavior is manifested by a spinodal point of a thermodynamic phase transition. Demonstrating this requires the introduction of an "order parameter" that is suitable for distinguishing between disordered amorphous systems. At the spinodal point there exists a divergent correlation length which is associated with the system-spanning instabilities (known also as shear bands) which are typical to the mechanical yield. The theory, the order parameter used and the correlation functions which exhibit the divergent correlation length are universal in nature and can be applied to any amorphous solid that undergoes mechanical yield. The phenomenon is seen at its sharpest in athermal systems, as is explained below; in this paper we extend the discussion also to thermal systems, showing that at sufficiently high temperatures the spinodal phenomenon is destroyed by thermal fluctuations.
Power-law citation distributions are not scale-free
Golosovsky, Michael
2017-09-01
We analyze time evolution of statistical distributions of citations to scientific papers published in the same year. While these distributions seem to follow the power-law dependence we find that they are nonstationary and the exponent of the power-law fit decreases with time and does not come to saturation. We attribute the nonstationarity of citation distributions to different longevity of the low-cited and highly cited papers. By measuring citation trajectories of papers we found that citation careers of the low-cited papers come to saturation after 10-15 years while those of the highly cited papers continue to increase indefinitely: The papers that exceed some citation threshold become runaways. Thus, we show that although citation distribution can look as a power-law dependence, it is not scale free and there is a hidden dynamic scale associated with the onset of runaways. We compare our measurements to our recently developed model of citation dynamics based on copying-redirection-triadic closure and find explanations to our empirical observations.
2013-01-08
... Employment and Training Administration CalAmp Wireless Networks Corporation (CWNC), Satellite Products Division, Including On-Site Leased Workers From Select Staffing, Oxnard, CA; CalAmp Wireless Networks... Networks Corporation (CWNC), and that the manufacturing of wireless networking products was transferred...
Routing strategies in traffic network and phase transition in network ...
Indian Academy of Sciences (India)
The dynamics of information traffic over scale-free networks has been investigated systematically. A series of routing strategies of data packets have been proposed, including the local routing strategy, the next-nearest-neighbour routing strategy, and the mixed routing strategy based on local static and dynamic information.
Scale-free random graphs and Potts model
Indian Academy of Sciences (India)
real-world networks such as the world-wide web, the Internet, the coauthorship, the protein interaction networks and so on display power-law behaviors in the degree ... in this paper, we study the evolution of SF random graphs from the perspective of equilibrium statistical physics. The formulation in terms of the spin model ...
Expanding the DOCLINE network to include nonmedical libraries in the state of Nevada.
Potter, L A; Zenan, J S
1993-01-01
Most libraries cannot meet patron demands for biomedical information using only their in-house collections. Consequently, many types of libraries request biomedical information through interlibrary loan, and these include not only academic health sciences libraries, hospital and special libraries, but also general libraries. In Nevada, with its small population spread over a large geographic area, it has become critical to develop a statewide network for sharing biomedical information. As the...
Expanding the DOCLINE network to include nonmedical libraries in the state of Nevada.
Potter, L A; Zenan, J S
1993-01-01
Most libraries cannot meet patron demands for biomedical information using only their in-house collections. Consequently, many types of libraries request biomedical information through interlibrary loan, and these include not only academic health sciences libraries, hospital and special libraries, but also general libraries. In Nevada, with its small population spread over a large geographic area, it has become critical to develop a statewide network for sharing biomedical information. As the state resource library, the Savitt Medical Library launched an effort to establish a network, via DOCLINE, of all Nevada libraries that have health-related collections. The process of convincing academic and community college libraries to join DOCLINE and the resulting benefits of improved resource sharing and cooperative collection development are discussed.
When is a Scale-Free Graph Ultra-Small?
van der Hofstad, Remco; Komjáthy, Júlia
2017-10-01
In this paper we study typical distances in the configuration model, when the degrees have asymptotically infinite variance. We assume that the empirical degree distribution follows a power law with exponent τ \\in (2,3), up to value n^{{β _n}} for some {β _n}≫ (log n)^{-γ } and γ \\in (0,1). This assumption is satisfied for power law i.i.d. degrees, and also includes truncated power-law empirical degree distributions where the (possibly exponential) truncation happens at n^{{β _n}}. These examples are commonly observed in many real-life networks. We show that the graph distance between two uniformly chosen vertices centers around 2 log log (n^{{β _n}}) / |log (τ -2)| + 1/({β _n}(3-τ )), with tight fluctuations. Thus, the graph is an ultrasmall world whenever 1/{β _n}=o(log log n). We determine the distribution of the fluctuations around this value, in particular we prove these form a sequence of tight random variables with distributions that show log log -periodicity, and as a result it is non-converging. We describe the topology and number of shortest paths: We show that the number of shortest paths is of order n^{f_n{β _n}}, where f_n \\in (0,1) is a random variable that oscillates with n. We decompose shortest paths into three segments, two `end-segments' starting at each of the two uniformly chosen vertices, and a middle segment. The two end-segments of any shortest path have length log log (n^{{β _n}}) / |log (τ -2)|+tight, and the total degree is increasing towards the middle of the path on these segments. The connecting middle segment has length 1/({β _n}(3-τ ))+tight, and it contains only vertices with degree at least of order n^{(1-f_n){β _n}}, thus all the degrees on this segment are comparable to the maximal degree. Our theorems also apply when instead of truncating the degrees, we start with a configuration model and we remove every vertex with degree at least n^{{β _n}}, and the edges attached to these vertices. This sheds light on
Rajchakit, G; Saravanakumar, R; Ahn, Choon Ki; Karimi, Hamid Reza
2017-02-01
This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples. Copyright © 2016 Elsevier Ltd. All rights reserved.
Routing strategies in traffic network and phase transition in network ...
Indian Academy of Sciences (India)
Routing strategy; network traffic flow; hysteretic loop; phase transition from free flow state to congestion state; scale-free network; bi-stable state; traffic dynamics. PACS Nos 89.75.Hc; 89.20.Hh; 05.10.-a; 89.75.Fb. 1. Traffic dynamics based on local routing strategy on scale-free networks. Communication networks such as ...
Jemai, Olfa; Ejbali, Ridha; Zaied, Mourad; Ben Amar, Chokri
2015-02-01
This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.
Grothausmann, Roman; Knudsen, Lars; Ochs, Matthias; Mühlfeld, Christian
2017-02-01
Grothausmann R, Knudsen L, Ochs M, Mühlfeld C. Digital 3D reconstructions using histological serial sections of lung tissue including the alveolar capillary network. Am J Physiol Lung Cell Mol Physiol 312: L243-L257, 2017. First published December 2, 2016; doi:10.1152/ajplung.00326.2016-The alveolar capillary network (ACN) provides an enormously large surface area that is necessary for pulmonary gas exchange. Changes of the ACN during normal or pathological development or in pulmonary diseases are of great functional impact and warrant further analysis. Due to the complexity of the three-dimensional (3D) architecture of the ACN, 2D approaches are limited in providing a comprehensive impression of the characteristics of the normal ACN or the nature of its alterations. Stereological methods offer a quantitative way to assess the ACN in 3D in terms of capillary volume, surface area, or number but lack a 3D visualization to interpret the data. Hence, the necessity to visualize the ACN in 3D and to correlate this with data from the same set of data arises. Such an approach requires a large sample volume combined with a high resolution. Here, we present a technically simple and cost-efficient approach to create 3D representations of lung tissue ranging from bronchioles over alveolar ducts and alveoli up to the ACN from more than 1 mm sample extent to a resolution of less than 1 μm. The method is based on automated image acquisition of serially sectioned epoxy resin-embedded lung tissue fixed by vascular perfusion and subsequent automated digital reconstruction and analysis of the 3D data. This efficient method may help to better understand mechanisms of vascular development and pathology of the lung. Copyright © 2017 the American Physiological Society.
Directory of Open Access Journals (Sweden)
reza hejazi
2012-02-01
Full Text Available Data envelopment analysis (DEA is known as one of the most common approaches for efficiency evaluation. Network models are new subjects in which, a DMU with all its subunits and links is considered as a network structure. One of the most widely used DEA methods for network data is the suggested approach of Lewis and Sexton. In this approach, performance of each DMU is measured compared to a similar DMU by moving on the effective paths and then computing the final outputs and classic primary inputs . In reality, many cases can be found that an original input or an intermediate product allocates to several subunits or forms a tour in a network. In such networks, the approach of Lewis and Sexton is not able to calculate efficiency. Therefore, in this paper, an approach has been proposed for solving such problems and computing the efficiency of such networks.
Interactions between the Design and Operation of Shale Gas Networks, Including CO2 Sequestration
Directory of Open Access Journals (Sweden)
Sharifzadeh Mahdi
2017-04-01
Full Text Available As the demand for energy continues to increase, shale gas, as an unconventional source of methane (CH4, shows great potential for commercialization. However, due to the ultra-low permeability of shale gas reservoirs, special procedures such as horizontal drilling, hydraulic fracturing, periodic well shut-in, and carbon dioxide (CO2 injection may be required in order to boost gas production, maximize economic benefits, and ensure safe and environmentally sound operation. Although intensive research is devoted to this emerging technology, many researchers have studied shale gas design and operational decisions only in isolation. In fact, these decisions are highly interactive and should be considered simultaneously. Therefore, the research question addressed in this study includes interactions between design and operational decisions. In this paper, we first establish a full-physics model for a shale gas reservoir. Next, we conduct a sensitivity analysis of important design and operational decisions such as well length, well arrangement, number of fractures, fracture distance, CO2 injection rate, and shut-in scheduling in order to gain in-depth insights into the complex behavior of shale gas networks. The results suggest that the case with the highest shale gas production may not necessarily be the most profitable design; and that drilling, fracturing, and CO2 injection have great impacts on the economic viability of this technology. In particular, due to the high costs, enhanced gas recovery (EGR using CO2 does not appear to be commercially competitive, unless tax abatements or subsidies are available for CO2 sequestration. It was also found that the interactions between design and operational decisions are significant and that these decisions should be optimized simultaneously.
Development of Next Generation Heating System for Scale Free Steel Reheating
Energy Technology Data Exchange (ETDEWEB)
Dr. Arvind C. Thekdi
2011-01-27
The work carried out under this project includes development and design of components, controls, and economic modeling tools that would enable the steel industry to reduce energy intensity through reduction of scale formation during the steel reheating process. Application of scale free reheating offers savings in energy used for production of steel that is lost as scale, and increase in product yield for the global steel industry. The technology can be applied to a new furnace application as well as retrofit design for conversion of existing steel reheating furnaces. The development work has resulted in the knowledge base that will enable the steel industry and steel forging industry us to reheat steel with 75% to 95% reduction in scale formation and associated energy savings during the reheating process. Scale reduction also results in additional energy savings associated with higher yield from reheat furnaces. Energy used for steel production ranges from 9 MM Btu/ton to 16.6 MM Btu/ton or the industry average of approximately 13 MM Btu/ton. Hence, reduction in scale at reheating stage would represent a substantial energy reduction for the steel industry. Potential energy savings for the US steel industry could be in excess of 25 Trillion Btu/year when the technology is applied to all reheating processes. The development work has resulted in new design of reheating process and the required burners and control systems that would allow use of this technology for steel reheating in steel as well as steel forging industries.
Directory of Open Access Journals (Sweden)
Grosso Juan M.
2016-09-01
Full Text Available This paper proposes a reliability-based economic model predictive control (MPC strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.
2013-04-12
... outages during the ``derecho'' windstorm that affected large portions of the United States in June 2012... (PSHSB or Bureau) January 10, 2013, report titled Impact of the June 2012 Derecho on Communications Networks and Services: Report and Recommendations (Derecho Report), which is available at http://www.fcc...
Bio-Inspired Computation: Clock-Free, Grid-Free, Scale-Free and Symbol Free
2015-06-11
AFRL-AFOSR-JP-TR-2015-0002 Bio -inspired computation: clock-free, grid-free, scale-free, and symbol free Janet Wiles THE UNIVERSITY OF QUEENSLAND...SUBTITLE Bio -inspired computation: clock-free, grid-free, scale-free, and symbol free 5a. CONTRACT NUMBER FA2386-12-1-4050 5b. GRANT NUMBER 5c...SUPPLEMENTARY NOTES 14. ABSTRACT The project developed a new fundamental component for bio -inspired computing, based on a new way of modelling
Endogenous network of firms and systemic risk
Ma, Qianting; He, Jianmin; Li, Shouwei
2018-02-01
We construct an endogenous network characterized by commercial credit relationships connecting the upstream and downstream firms. Simulation results indicate that the endogenous network model displays a scale-free property which exists in real-world firm systems. In terms of the network structure, with the expansion of the scale of network nodes, the systemic risk increases significantly, while the heterogeneities of network nodes have no effect on systemic risk. As for firm micro-behaviors, including the selection range of trading partners, actual output, labor requirement, price of intermediate products and employee salaries, increase of all these parameters will lead to higher systemic risk.
Directory of Open Access Journals (Sweden)
H. L. Sneha
2013-01-01
Full Text Available The current focus in defense arena is towards the stealth technology with an emphasis to control the radar cross-section (RCS. The scattering from the antennas mounted over the platform is of prime importance especially for a low-observable aerospace vehicle. This paper presents the analysis of the scattering cross section of a uniformly spaced linear dipole array. Two types of feed networks, that is, series and parallel feed networks, are considered. The total RCS of phased array with either kind of feed network is obtained by following the signal as it enters through the aperture and travels through the feed network. The RCS estimation of array is done including the mutual coupling effect between the dipole elements in three configurations, that is, side-by-side, collinear, and parallel-in-echelon. The results presented can be useful while designing a phased array with optimum performance towards low observability.
Detrended fluctuation analysis: A scale-free view on neuronal oscillations
Hardstone, R.E.; Poil, S.S.; Schiavone, G.; Nikulin, V.V.; Mansvelder, H.D.; Linkenkaer Hansen, K.
2012-01-01
Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease
Janssen, G.M.C.M.; Valstar, J.R.; Zee, S.E.A.T.M. van der
2008-01-01
Traveltime determinations have found increasing application in the characterization of groundwater systems. No algorithms are available, however, to optimally design sampling strategies including this information type. We propose a first-order methodology to include groundwater age or tracer arrival
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
a node, has an exponential tail, in contrast to the algebraic one that characterizes scale-free networks recently discovered in a variety of real-world situations [3,4]. Scale-free networks are heterogeneous as their connectivity can vary significantly from node to node and a considerable number of links can be associated with ...
Navigation in small-world networks: a scale-free continuum model
Franceschetti, M.; Meester, R.W.J.
2006-01-01
The small-world phenomenon, the principle that we are all linked by a short chain of intermediate acquaintances, has been investigated in mathematics and social sciences. It has been shown to be pervasive both in nature and in engineering systems like the World Wide Web. Work of Jon Kleinberg has
Barkaoui, Abdelwahed; Tarek, Merzouki; Hambli, Ridha; Ali, Mkaddem
2014-01-01
The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher s...
Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory
Pearce, Roger
2013-05-01
We present techniques to process large scale-free graphs in distributed memory. Our aim is to scale to trillions of edges, and our research is targeted at leadership class supercomputers and clusters with local non-volatile memory, e.g., NAND Flash. We apply an edge list partitioning technique, designed to accommodate high-degree vertices (hubs) that create scaling challenges when processing scale-free graphs. In addition to partitioning hubs, we use ghost vertices to represent the hubs to reduce communication hotspots. We present a scaling study with three important graph algorithms: Breadth-First Search (BFS), K-Core decomposition, and Triangle Counting. We also demonstrate scalability on BG/P Intrepid by comparing to best known Graph500 results. We show results on two clusters with local NVRAM storage that are capable of traversing trillion-edge scale-free graphs. By leveraging node-local NAND Flash, our approach can process thirty-two times larger datasets with only a 39% performance degradation in Traversed Edges Per Second (TEPS). © 2013 IEEE.
Detrended fluctuation analysis: A scale-free view on neuronal oscillations
Directory of Open Access Journals (Sweden)
Richard eHardstone
2012-11-01
Full Text Available Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT and links to the NBT tutorial website (http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.
Detrended fluctuation analysis: a scale-free view on neuronal oscillations.
Hardstone, Richard; Poil, Simon-Shlomo; Schiavone, Giuseppina; Jansen, Rick; Nikulin, Vadim V; Mansvelder, Huibert D; Linkenkaer-Hansen, Klaus
2012-01-01
Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT) and links to the NBT tutorial website http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.
Furtak-Wrona, K.; Kozik-Ostrówka, P.; Jadwiszczak, K.; Maigret, J. E.; Aguié-Béghin, V.; Coqueret, X.
2018-01-01
A water-based polyurethane (PUR) acrylate water emulsion was selected as a radiation curable matrix for preparing nanocomposites including cellulose nanocrystals (CNC) prepared by controlled hydrolysis of Ramie fibers. Cross-linking polymerization of samples prepared in the form of films or of 1 mm-thick bars was either initiated by exposure to the 395 nm light of a high intensity LED lamp or by treatment with low energy electron beam (EB). The conversion level of acrylate functions in samples submitted to increasing radiation doses was monitored by Fourier Transform Infrared Spectroscopy (FTIR). Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Analysis (DMA) were used to characterize changes in the glass transition temperature of the PUR-CNC nanocomposites as a function of acrylate conversion and of CNC content. Micromechanical testing indicates the positive effect of 1 wt% CNC on Young's modulus and on the tensile strength at break (σ) of cured nanocomposites. The presence of CNC in the PUR acrylate matrix was shown to double the σ value of the nanocomposite cured to an acrylate conversion level of 85% by treatment with a 25 kGy dose under EB, whereas no increase of σ was observed in UV-cured samples exhibiting the same acrylate conversion level. The occurrence of grafting reactions inducing covalent linkages between the polysaccharide nanofiller and the PUR acrylate matrix during the EB treatment is advanced as an explanation to account for the improvement observed in samples cured under ionizing radiation.
Energy Technology Data Exchange (ETDEWEB)
Kouchri, Farrokh Mohammadzadeh
2012-11-06
A Voice over Internet Protocol (VoIP) communications system, a method of managing a communications network in such a system and a program product therefore. The system/network includes an ENERGY STAR (E-star) aware softswitch and E-star compliant communications devices at system endpoints. The E-star aware softswitch allows E-star compliant communications devices to enter and remain in power saving mode. The E-star aware softswitch spools messages and forwards only selected messages (e.g., calls) to the devices in power saving mode. When the E-star compliant communications devices exit power saving mode, the E-star aware softswitch forwards spooled messages.
Filament Formation in Molecular Clouds as a Scale-Free Process
Vázquez-Semadeni, Enrique; Gómez, Gilberto
We discuss the formation of filaments in molecular clouds (MCs) as the result of large-scale collapse in the clouds. We first give arguments suggesting that self-gravity dominates the nonthermal motions, and then briefly describe the resulting structure, similar to that found in molecular-line and dust observations of the filaments in the clouds. The filaments exhibit a hierarchical structure in both density and velocity, suggesting a scale-free nature, similar to that of the cosmic web, resulting from the domination of self-gravity from the MC down to the core scale.
Directory of Open Access Journals (Sweden)
Supriya Aggarwal
2012-01-01
Full Text Available One of the most important steps in spectral analysis is filtering, where window functions are generally used to design filters. In this paper, we modify the existing architecture for realizing the window functions using CORDIC processor. Firstly, we modify the conventional CORDIC algorithm to reduce its latency and area. The proposed CORDIC algorithm is completely scale-free for the range of convergence that spans the entire coordinate space. Secondly, we realize the window functions using a single CORDIC processor as against two serially connected CORDIC processors in existing technique, thus optimizing it for area and latency. The linear CORDIC processor is replaced by a shift-add network which drastically reduces the number of pipelining stages required in the existing design. The proposed design on an average requires approximately 64% less pipeline stages and saves up to 44.2% area. Currently, the processor is designed to implement Blackman windowing architecture, which with slight modifications can be extended to other widow functions as well. The details of the proposed architecture are discussed in the paper.
Energy Technology Data Exchange (ETDEWEB)
Guerrero Angulo, Jose Oscar [Universidad Autonoma de Sinaloa (Mexico); Arreguin Cortes, Felipe [Instituto Mexicano de Tecnologia del Agua, Jiutepec, Morelos (Mexico)
2002-03-01
This paper presents a hydraulic simulation model for drinking water networks, including elements that are currently not considered household connections, spatially variable flowrate distribution pipelines, and tee secondary network. This model is determined by solving the equations needed for a conventional model following an indirect procedure for the solution of large equations systems. Household connection performance is considered as dependent of water pressure and the way in which users operate the taps of such intakes. This approach allows a better a acquaintance with the drinking water supply networks performance as well as solving problems that demand a more precise hydraulic simulation, such as water quality variations, leaks in networks, and the influence of home water tanks as regulating devices. [Spanish] Se presenta un modelo de simulacion hidraulica para redes de agua potable en el cual se incluyen elementos que no se toman en cuenta actualmente, como las tomas domiciliarias, los tubos de distribucion con gastos espacialmente variado y la red secundaria, resolviendo el numero de ecuaciones que seria necesario plantear en un modelo convencional mediante un procedimiento indirecto para la solucion de grandes sistemas de ecuaciones. En las tomas domiciliarias se considera que su funcionamiento depende de las presiones y la forma en que los usuarios operan las llaves de las mismas. Este planteamiento permite conocer mejor el funcionamiento de las redes de abastecimiento de agua potable y solucionar problemas que requieren de una simulacion hidraulica mas precisa, como el comportamiento de la calidad del agua, las fugas en las redes y la influencia reguladora de los tinacos de las casas.
Häggman-Henrikson, B; Alstergren, P; Davidson, T; Högestätt, E D; Östlund, P; Tranaeus, S; Vitols, S; List, T
2017-10-01
This health technology assessment evaluated the efficacy of pharmacological treatment in patients with oro-facial pain. Randomised controlled trials were included if they reported pharmacological treatment in patients ≥18 years with chronic (≥3 months) oro-facial pain. Patients were divided into subgroups: TMD-muscle [temporomandibular disorders (TMD) mainly associated with myalgia]; TMD-joint (TMD mainly associated with temporomandibular joint pain); and burning mouth syndrome (BMS). The primary outcome was pain intensity reduction after pharmacological treatment. The scientific quality of the evidence was rated according to GRADE. An electronic search in PubMed, Cochrane Library, and EMBASE from database inception to 1 March 2017 combined with a handsearch identified 1552 articles. After screening of abstracts, 178 articles were reviewed in full text and 57 studies met the inclusion criteria. After risk of bias assessment, 41 articles remained: 15 studies on 790 patients classified as TMD-joint, nine on 375 patients classified as TMD-muscle and 17 on 868 patients with BMS. Of these, eight studies on TMD-muscle, and five on BMS were included in separate network meta-analysis. The narrative synthesis suggests that NSAIDs as well as corticosteroid and hyaluronate injections are effective treatments for TMD-joint pain. The network meta-analysis showed that clonazepam and capsaicin reduced pain intensity in BMS, and the muscle relaxant cyclobenzaprine, for the TMD-muscle group. In conclusion, based on a limited number of studies, evidence provided with network meta-analysis showed that clonazepam and capsaicin are effective in treatment of BMS and that the muscle relaxant cyclobenzaprine has a positive treatment effect for TMD-muscle pain. © 2017 John Wiley & Sons Ltd.
Häggman-Henrikson, B; Alstergren, P; Davidson, T; Högestätt, Ed; Östlund, P; Tranaeus, S; Vitols, S; List, T
2017-06-27
This health technology assessment evaluated the efficacy of pharmacological treatment in patients with orofacial pain. Randomised controlled trials were included if they reported pharmacological treatment in patients ≥18 years with chronic (≥3 months) orofacial pain. Patients were divided into subgroups: TMD-muscle [Temporomandibular disorders (TMD) mainly associated with myalgia]; TMD-joint (TMD mainly associated with temporomandibular joint pain); and Burning mouth syndrome (BMS). The primary outcome was pain intensity reduction after pharmacological treatment. The scientific quality of the evidence was rated according to GRADE. An electronic search in PubMed, Cochrane Library, and Embase from database inception to 1 March 2017 combined with a handsearch identified 1,556 articles. After screening of abstracts, 182 articles were reviewed in full text and 57 studies met the inclusion criteria. After risk of bias assessment, 41 articles remained: 15 studies on 790 patients classified as TMD-joint, 9 on 375 patients classified as TMD-muscle, and 17 on 868 patients with BMS. Of these, 8 studies on TMD-muscle and 5 on BMS were included in separate network meta-analysis. The narrative synthesis suggests that NSAIDs as well as corticosteroid and hyaluronate injections are effective treatments for TMD-joint pain. The network meta-analysis showed that clonazepam and capsaicin reduced pain intensity in BMS, and the muscle relaxant cyclobenzaprine, for the TMD-muscle group. In conclusion, based on a limited number of studies, evidence provided with network meta-analysis showed that clonazepam and capsaicin are effective in treatment of BMS and that the muscle relaxant cyclobenzaprine have a positive treatment effect for TMD-muscle pain. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Scale-free distribution of Dead Sea sinkholes: Observations and modeling
Yizhaq, H.; Ish-Shalom, C.; Raz, E.; Ashkenazy, Y.
2017-05-01
There are currently more than 5500 sinkholes along the Dead Sea in Israel. These were formed due to the dissolution of subsurface salt layers as a result of the replacement of hypersaline groundwater by fresh brackish groundwater. This process has been associated with a sharp decline in the Dead Sea water level, currently more than 1 m/yr, resulting in a lower water table that has allowed the intrusion of fresher brackish water. We studied the distribution of the sinkhole sizes and found that it is scale free with a power law exponent close to 2. We constructed a stochastic cellular automata model to understand the observed scale-free behavior and the growth of the sinkhole area in time. The model consists of a lower salt layer and an upper soil layer in which cavities that develop in the lower layer lead to collapses in the upper layer. The model reproduces the observed power law distribution without involving the threshold behavior commonly associated with criticality.
Scale-free brain-wave music from simultaneously EEG and fMRI recordings.
Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong
2012-01-01
In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain.
Large fluctuations in anti-coordination games on scale-free graphs
Sabsovich, Daniel; Mobilia, Mauro; Assaf, Michael
2017-05-01
We study the influence of the complex topology of scale-free graphs on the dynamics of anti-coordination games (e.g. snowdrift games). These reference models are characterized by the coexistence (evolutionary stable mixed strategy) of two competing species, say ‘cooperators’ and ‘defectors’, and, in finite systems, by metastability and large-fluctuation-driven fixation. In this work, we use extensive computer simulations and an effective diffusion approximation (in the weak selection limit) to determine under which circumstances, depending on the individual-based update rules, the topology drastically affects the long-time behavior of anti-coordination games. In particular, we compute the variance of the number of cooperators in the metastable state and the mean fixation time when the dynamics is implemented according to the voter model (death-first/birth-second process) and the link dynamics (birth/death or death/birth at random). For the voter update rule, we show that the scale-free topology effectively renormalizes the population size and as a result the statistics of observables depend on the network’s degree distribution. In contrast, such a renormalization does not occur with the link dynamics update rule and we recover the same behavior as on complete graphs.
Scale-free brain-wave music from simultaneously EEG and fMRI recordings.
Directory of Open Access Journals (Sweden)
Jing Lu
Full Text Available In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain.
Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings
Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong
2012-01-01
In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain. PMID:23166768
Scale-free distribution of Dead Sea sinkholes--observations and modeling
Yizhaq, Hezi; Raz, Eli; Ashkenazy, Yosef
2016-01-01
There are currently more than 5500 sinkholes along the Dead Sea in Israel. These were formed due to dissolution of subsurface salt layers as a result of the replacement of hypersaline groundwater by fresh brackish groundwater. This process was associated with a sharp decline in the Dead Sea level, currently more than one meter per year, resulting in a lower water table that has allowed the intrusion of fresher brackish water. We studied the distribution of the sinkholes sizes and found that it is scale-free with a power-law exponent close to 2. We constructed a stochastic cellular automata model to understand the observed scale-free behavior and the growth of the sinkholes area in time. The model consists of a lower salt layer and an upper soil layer in which cavities that develop in the lower layer lead to collapses in the upper layer. The model reproduces the observed power-law distribution without entailing the threshold behavior commonly associated with criticality.
Note on the Complex Networks and Epidemiology Part I: Complex Networks
Kim, James
2013-01-01
Complex networks describe a wide range of systems in nature and society. Frequently cited examples include Internet, WWW, a network of chemicals linked by chemical reactions, social relationship networks, citation networks, etc. The research of complex networks has attracted many scientists' attention. Physicists have shown that these networks exhibit some surprising characters, such as high clustering coefficient, small diameter, and the absence of the thresholds of percolation. Scientists in mathematical epidemiology discovered that the threshold of infectious disease disappears on contact networks that following Scale-Free distribution. Researchers in economics and public health also find that the imitation behavior could lead to cluster phenomena of vaccination and un-vaccination. In this note, we will review the basic concepts of complex networks; Basic epidemic models; the development of complex networks and epidemiology.
Scale-free foraging by primates emerges from their interaction with a complex environment
Boyer, Denis; Ramos-Fernández, Gabriel; Miramontes, Octavio; Mateos, José L; Cocho, Germinal; Larralde, Hernán; Ramos, Humberto; Rojas, Fernando
2006-01-01
Scale-free foraging patterns are widespread among animals. These may be the outcome of an optimal searching strategy to find scarce, randomly distributed resources, but a less explored alternative is that this behaviour may result from the interaction of foraging animals with a particular distribution of resources. We introduce a simple foraging model where individual primates follow mental maps and choose their displacements according to a maximum efficiency criterion, in a spatially disordered environment containing many trees with a heterogeneous size distribution. We show that a particular tree-size frequency distribution induces non-Gaussian movement patterns with multiple spatial scales (Lévy walks). These results are consistent with field observations of tree-size variation and spider monkey (Ateles geoffroyi) foraging patterns. We discuss the consequences that our results may have for the patterns of seed dispersal by foraging primates. PMID:16790406
Turbulence and other processes for the scale-free texture of the fast solar wind
Hnat, B.; Chapman, S. C.; Gogoberidze, G.; Wicks, R. T.
2012-04-01
The higher-order statistics of magnetic field magnitude fluctuations in the fast quiet solar wind are quantified systematically, scale by scale. We find a single global non-Gaussian scale-free behavior from minutes to over 5 hours. This spans the signature of an inertial range of magnetohydrodynamic turbulence and a ˜1/f range in magnetic field components. This global scaling in field magnitude fluctuations is an intrinsic component of the underlying texture of the solar wind which co-exists with the signature of MHD turbulence but extends to lower frequencies. Importantly, scaling and non- Gaussian statistics of fluctuations are not unique to turbulence and can imply other physical mechanisms- our results thus place a strong constraint on theories of the dynamics of the solar corona and solar wind. Intriguingly, the magnetic field and velocity components also show scale-dependent dynamic alignment outside of the inertial range of MHD turbulence.
Network graph analysis of category fluency testing.
Lerner, Alan J; Ogrocki, Paula K; Thomas, Peter J
2009-03-01
Category fluency is impaired early in Alzheimer disease (AD). Graph theory is a technique to analyze complex relationships in networks. Features of interest in network analysis include the number of nodes and edges, and variables related to their interconnectedness. Other properties important in network analysis are "small world properties" and "scale-free" properties. The small world property (popularized as the so-called "6 degrees of separation") arises when the majority of connections are local, but a number of connections are to distant nodes. Scale-free networks are characterized by the presence of a few nodes with many connections, and many more nodes with fewer connections. To determine if category fluency data can be analyzed using graph theory. To compare normal elderly, mild cognitive impairment (MCI) and AD network graphs, and characterize changes seen with increasing cognitive impairment. Category fluency results ("animals" recorded over 60 s) from normals (n=38), MCI (n=33), and AD (n=40) completing uniform data set evaluations were converted to network graphs of all unique cooccurring neighbors, and compared for network variables. For Normal, MCI and AD, mean clustering coefficients were 0.21, 0.22, 0.30; characteristic path lengths were 3.27, 3.17, and 2.65; small world properties decreased with increasing cognitive impairment, and all graphs showed scale-free properties. Rank correlations of the 25 commonest items ranged from 0.75 to 0.83. Filtering of low-degree nodes in normal and MCI graphs resulted in properties similar to the AD network graph. Network graph analysis is a promising technique for analyzing changes in category fluency. Our technique results in nonrandom graphs consistent with well-characterized properties for these types of graphs.
Network structure of production
Atalay, Enghin; Hortaçsu, Ali; Roberts, James; Syverson, Chad
2011-01-01
Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena. PMID:21402924
Scale-free brain quartet: artistic filtering of multi-channel brainwave music.
Wu, Dan; Li, Chaoyi; Yao, Dezhong
2013-01-01
To listen to the brain activities as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which translated scalp EEGs into music notes according to the power law of both EEG and music. In the present study, the methodology was extended for deriving a quartet from multi-channel EEGs with artistic beat and tonality filtering. EEG data from multiple electrodes were first translated into MIDI sequences by SFBM, respectively. Then, these sequences were processed by a beat filter which adjusted the duration of notes in terms of the characteristic frequency. And the sequences were further filtered from atonal to tonal according to a key defined by the analysis of the original music pieces. Resting EEGs with eyes closed and open of 40 subjects were utilized for music generation. The results revealed that the scale-free exponents of the music before and after filtering were different: the filtered music showed larger variety between the eyes-closed (EC) and eyes-open (EO) conditions, and the pitch scale exponents of the filtered music were closer to 1 and thus it was more approximate to the classical music. Furthermore, the tempo of the filtered music with eyes closed was significantly slower than that with eyes open. With the original materials obtained from multi-channel EEGs, and a little creative filtering following the composition process of a potential artist, the resulted brainwave quartet opened a new window to look into the brain in an audible musical way. In fact, as the artistic beat and tonal filters were derived from the brainwaves, the filtered music maintained the essential properties of the brain activities in a more musical style. It might harmonically distinguish the different states of the brain activities, and therefore it provided a method to analyze EEGs from a relaxed audio perspective.
Scale-free brain quartet: artistic filtering of multi-channel brainwave music.
Directory of Open Access Journals (Sweden)
Dan Wu
Full Text Available To listen to the brain activities as a piece of music, we proposed the scale-free brainwave music (SFBM technology, which translated scalp EEGs into music notes according to the power law of both EEG and music. In the present study, the methodology was extended for deriving a quartet from multi-channel EEGs with artistic beat and tonality filtering. EEG data from multiple electrodes were first translated into MIDI sequences by SFBM, respectively. Then, these sequences were processed by a beat filter which adjusted the duration of notes in terms of the characteristic frequency. And the sequences were further filtered from atonal to tonal according to a key defined by the analysis of the original music pieces. Resting EEGs with eyes closed and open of 40 subjects were utilized for music generation. The results revealed that the scale-free exponents of the music before and after filtering were different: the filtered music showed larger variety between the eyes-closed (EC and eyes-open (EO conditions, and the pitch scale exponents of the filtered music were closer to 1 and thus it was more approximate to the classical music. Furthermore, the tempo of the filtered music with eyes closed was significantly slower than that with eyes open. With the original materials obtained from multi-channel EEGs, and a little creative filtering following the composition process of a potential artist, the resulted brainwave quartet opened a new window to look into the brain in an audible musical way. In fact, as the artistic beat and tonal filters were derived from the brainwaves, the filtered music maintained the essential properties of the brain activities in a more musical style. It might harmonically distinguish the different states of the brain activities, and therefore it provided a method to analyze EEGs from a relaxed audio perspective.
EFFECTIVENESS OF OPINION INFLUENCE APPROACHES IN HIGHLY CLUSTERED ONLINE SOCIAL NETWORKS
MELISSA FALETRA; NATHAN PALMER; Marshall, Jeffrey S.
2014-01-01
A mathematical model was developed for opinion propagation on online social networks using a scale-free network with an adjustable clustering coefficient. Connected nodes influence each other when the difference between their opinion values is less than a threshold value. The model is used to examine effectiveness of three different approaches for influencing public opinion. The approaches examined include (1) a "Class", defined as an approach (such as a class or book) that greatly influences...
Concentration dependent model of protein-protein interaction networks
Zhang, Jingshan
2007-01-01
The scale free structure p(k)~k^{-gamma} of protein-protein interaction networks can be produced by a static physical model. We find the earlier study of deterministic threshold models with exponential fitness distributions can be generalized to explain the apparent scale free degree distribution of the physical model, and this explanation provides a generic mechanism of "scale free" networks. We predict the dependence of gamma on experimental protein concentrations. The clustering coefficient distribution of the model is also studied.
Directory of Open Access Journals (Sweden)
C W Lin
2015-03-01
Full Text Available The Visinin-like 1 (VSNL1 gene encodes Visinin-like protein 1, a peripheral biomarker for Alzheimer disease (AD. Little is known, however, about normal VSNL1 expression in brain and the biologic networks in which it participates. Frontal cortex gray matter from 209 subjects without neurodegenerative or psychiatric illness, ranging in age from 16–91, were processed on Affymetrix GeneChip 1.1 ST and Human SNP Array 6.0. VSNL1 expression was unaffected by age and sex, and not significantly associated with SNPs in cis or trans. VSNL1 was significantly co-expressed with genes in pathways for Calcium Signaling, AD, Long Term Potentiation, Long Term Depression, and Trafficking of AMPA Receptors. The association with AD was driven, in part, by correlation with amyloid precursor protein (APP expression. These findings provide an unbiased link between VSNL1 and molecular mechanisms of AD, including pathways implicated in synaptic pathology in AD. Whether APP may drive increased VSNL1 expression, VSNL1 drives increased APP expression, or both are downstream of common pathogenic regulators will need to be evaluated in model systems.
Morawietz, Tobias; Behler, Jörg
2013-08-15
The fundamental importance of water for many chemical processes has motivated the development of countless efficient but approximate water potentials for large-scale molecular dynamics simulations, from simple empirical force fields to very sophisticated flexible water models. Accurate and generally applicable water potentials should fulfill a number of requirements. They should have a quality close to quantum chemical methods, they should explicitly depend on all degrees of freedom including all relevant many-body interactions, and they should be able to describe molecular dissociation and recombination. In this work, we present a high-dimensional neural network (NN) potential for water clusters based on density-functional theory (DFT) calculations, which is constructed using clusters containing up to 10 monomers and is in principle able to meet all these requirements. We investigate the reliability of specific parametrizations employing two frequently used generalized gradient approximation (GGA) exchange-correlation functionals, PBE and RPBE, as reference methods. We find that the binding energy errors of the NN potentials with respect to DFT are significantly lower than the typical uncertainties of DFT calculations arising from the choice of the exchange-correlation functional. Further, we examine the role of van der Waals interactions, which are not properly described by GGA functionals. Specifically, we incorporate the D3 scheme suggested by Grimme (J. Chem. Phys. 2010, 132, 154104) in our potentials and demonstrate that it can be applied to GGA-based NN potentials in the same way as to DFT calculations without modification. Our results show that the description of small water clusters provided by the RPBE functional is significantly improved if van der Waals interactions are included, while in case of the PBE functional, which is well-known to yield stronger binding than RPBE, van der Waals corrections lead to overestimated binding energies.
Schertzer, Daniel
2015-04-01
The EGU's 2015 theme 'a voyage through scales' is a recognition of the wild variability of geophysical fields over wide ranges of scales. However, we cannot forget Samuel Becket's criticism of all voyages: 'We don't travel for the fun of it, as far as I know; we're foolish, but not that foolish.' Such travels would be in fact hardly manageable: atmospheric dynamics are already beyond the yotta scale (1024)! Fortunately, Pandora's box has been opened enough to take us on a motionless travel across scales à la Gulliver. Scale symmetry is becoming generalized to the point that geophysical systems can be perceived as fixed points of (generalized) space-time contractions/dilations, depending on the side of the Wonderland mushrooms bitten by Alice. The now dated scale dependent observables are going to be replaced by scale independent singularities yielding scale free (nonlinear) geophysics. The (not yet solved) millennium problem of hydrodynamic turbulence is surprisingly a pedagogical example to illustrate what is at stake and motivated a series of paradigm shifts. Indeed, this problem can be stripped down to a network of triadic interactions. This graphically highlights how field components 'talk' to each other, i.e. how an infinitely small perturbation propagates through this network. This points out the dead ends of previous approaches (e.g. quasi-normal assumptions) and provide a first tier of concepts such as: multifractal cascades, singularities, universality, phase transitions and predictability limits. These concepts already provide a wealth of non trivial results, particularly the emergent 'dressed' properties generated by the whole set of interactions with respect to the 'bare' properties resulting from a scale truncation. Their extremes can be qualitatively different, having respectively 'heavy' and 'thin' tailed probability distributions. Moreover, the ubiquitous anisotropy of geophysical fields and patterns required another paradigm shift: a generalized
U.S. Environmental Protection Agency — The HIGHWAYS layer contains the Highway network, using NAVTEQ Functional Class=1,2,3 which includes major routes between minor cities or towns, and through city...
Hnat, B.; Chapman, S. C.; Gogoberidze, G.; Wicks, R. T.
2011-12-01
We present the first scale-by-scale quantitative comparison of the higher order statistics of magnetic field magnitude and component temporal fluctuations in the fast quiet solar wind. The magnetic field magnitude fluctuations show a single global intermittent non-Gaussian scale free behaviour from minutes to over 5 hours. This coexists with the signature in the field components of an inertial range of magnetohydrodynamic (MHD) turbulence up to ~ 30 minutes and a ~ 1/f range of coronal origin on longer timescales. This is found both in the ecliptic with ACE and in ULLYSES polar passes. This suggests a single stochastic process for magnetic field magnitude fluctuations operating across the full range of MHD timescales supported by the solar wind. Fluctuations in velocity and magnetic field show the strongest 'dynamic' alignment on scales in the ~ 1/f range. We wil discuss how uncertainties in velocity and magnetic field measurements propagate through 'compound' measures of the turbulence properties of the flow in this context. Observational evidence of incompressible MHD turbulence in the solar wind must thus be understood in the context of this global scaling of the compressive 'texture' of the solar wind.
Is the immune network a complex network?
Souza-e-Silva, Hallan
2012-01-01
Some years ago a cellular automata model was proposed to describe the evolution of the immune repertoire of B cells and antibodies based on Jerne's immune network theory and shape-space formalism. Here we investigate if the networks generated by this model in the different regimes can be classified as complex networks. We have found that in the chaotic regime the network has random characteristics with large, constant values of clustering coefficients, while in the ordered phase, the degree distribution of the network is exponential and the clustering coefficient exhibits power law behavior. In the transition region we observed a mixed behavior (random-like and exponential) of the degree distribution as opposed to the scale-free behavior reported for other biological networks. Randomness and low connectivity in the active sites allow for rapid changes in the connectivity distribution of the immune network in order to include and/or discard information and generate a dynamic memory. However it is the availabil...
Complex quantum network geometries: Evolution and phase transitions
Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao
2015-08-01
Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.
Network rewiring dynamics with convergence towards a star network.
Whigham, P A; Dick, G; Parry, M
2016-10-01
Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz (Nature393, 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.
Spatial Structure and Scaling of Agricultural Networks
Sousa, Daniel
2016-01-01
Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an a...
Quantifying complex network evolution using mutual entropy and dynamical clustering
Montealegre, Vladimir
The study of the structure and evolution of complex networks plays an important role for a diversity of areas which include computer science, biology, sociology, pattern recognition and cryptography. Here we use methods based on the principles of classical mechanics and statistical physics to study the topology and dynamics of complex networks. Dynamical clustering is used to detect the main structural blocks of networks (communities) and generalized mutual information of the Renyi type is used to rank the detected communities according to how crucial they are for the network's structure. Two approaches to measure the information content of networks are presented. One approach is based on the elements of the connectivity matrix, and the other on the concept of distance between nodes. These methods are applied for simulated networks of the scale-free, random and mixed types and for real world networks. The concept of triangles distribution as an extension to the degree distribution is also considered.
Conflicting attachment and the growth of bipartite networks
Yin, Chung; Weitz, Joshua S
2015-01-01
Simple growth mechanisms have been proposed to explain the emergence of seemingly universal network structures. The widely-studied model of preferential attachment assumes that new nodes are more likely to connect to highly connected nodes. Preferential attachment explains the emergence of scale-free degree distributions within complex networks. Yet, it is incompatible with many network systems, particularly bipartite systems in which two distinct types of agents interact. For example, the addition of new links in a host-parasite system corresponds to the infection of hosts by parasites. Increasing connectivity is beneficial to a parasite and detrimental to a host. Therefore, the overall network connectivity is subject to conflicting pressures. Here, we propose a stochastic network growth model of conflicting attachment, inspired by a particular kind of parasite-host interactions: that of viruses interacting with microbial hosts. The mechanism of network growth includes conflicting preferences to network dens...
Impact of informal networks on opinion dynamics in hierarchically formal organization
Song, Xiao; Shi, Wen; Ma, Yaofei; Yang, Chen
2015-10-01
Traditional opinion dynamics model focused mainly on the conditions under which a group of agents would reach a consensus. Conclusion has been gained that continuous opinion dynamics are subject to the constraint that convergent opinion adjustment only proceeds when opinion difference is below a given tolerance. This conclusion is useful but neglected the fact that an organization often consists of overlapped networks including formally hierarchical network and small-world/scale-free informal networks. To study the impact of different types of informal networks on converging speed or the number of opinion clusters, four typical types of informal networks (small-world, scale-free, tree and fully connected) are modeled and proposed as complements to formal communications. Experiments to compare formal network and hybrid networks are then carried out. It is observed that opinion dynamics with supplemented communications of informal networks can benefit convergence speed and reduce opinion clusters. More importantly, it is revealed that three key factors of informal networks affect their impact on formal network. These factors of informal network in descending orders are: agents' tolerances, scale and number of links.
Directory of Open Access Journals (Sweden)
A. Ahmadi Javid
2012-01-01
Full Text Available
ENGLISH ABSTRACT: In this paper, we present a distribution network design problem in a supply chain system that minimises the total cost of location, inventory, and delivery delay. Customers’ demands are random, and multiple capacity levels are available for the distribution centers. The problem is first formulated as a mixed integer convex programming model to optimally solve medium-sized instances, and then a heuristic is developed for solving large-sized instances.
AFRIKAANSE OPSOMMING: In hierdie artikel word ‘n distribusienetwerkprobleem in ‘n voorsieningsketting voorgehou waar die totale koste van die ligging, voorraad en afleweringsvertragings geminimiseer word. Die vraag is lukraak en verskeie kapasiteitsvlakke is beskikbaar in die verspreidingsentra. Die problem word eers geformuleer as ‘n gemengde-heeltal-konvekse model sodat mediumgrootte gevalle geoptimiseer kan word, waarna ‘n heuristieke benadering ontwikkel word vir die oplos van grootskaalse aktiwiteite.
McAteer, R. T. James
2015-08-01
My soul is spiraling in frozen fractals all around, And one thought crystallizes like an icy blast, I'm never going back, the past is in the past.Elsa, from Disney’s Frozen, characterizes two fundamental aspects of scale-free processes in Nature: fractals are everywhere in space; fractals can be used to probe changes in time. Self-Organized Criticality provides a powerful set of tools to study scale-free processes. It connects spatial fractals (more generically, multifractals) to temporal evolution. The drawback is that this usually results in scale-free, unit-less, indices, which can be difficult to connect to everyday physics. Here, I show a novel method that connects one of the most powerful SOC tools - the wavelet transform modulus maxima approach to calculating multifractality - to one of the most powerful equations in all of physics - Ampere’s law. In doing so I show how the multifractal spectra can be expressed in terms of current density, and how current density can then be used for the prediction of future energy release from such a system.Our physical understanding of the solar magnetic field structure, and hence our ability to predict solar activity, is limited by the type of data currently available. I show that the multifractal spectrum provides a powerful physical connection between the details of photospheric magnetic gradients of current data and the coronal magnetic structure. By decomposing Ampere’s law and comparing it to the wavelet transform modulus maximum method, I show how the scale-free Holder exponent provides a direct measure of current density across all relevant sizes. The prevalence of this current density across various scales is connected to its stability in time, and hence to the ability of the magnetic structure to store and then release energy. Hence (spatial) multifractals inform us of (future) solar activity.Finally I discuss how such an approach can be used in any study of scale-free processes, and highlight the necessary
Ebel, Holger; Davidsen, Joern; Bornholdt, Stefan
2003-01-01
Complex networks as the World Wide Web, the web of human sexual contacts or criminal networks often do not have an engineered architecture but instead are self-organized by the actions of a large number of individuals. From these local interactions non-trivial global phenomena can emerge as small-world properties or scale-free degree distributions. A simple model for the evolution of acquaintance networks highlights the essential dynamical ingredients necessary to obtain such complex network ...
The Solar Flare Complex Network
Gheibi, Akbar; Safari, Hossein; Javaherian, Mohsen
2017-10-01
We investigate the characteristics of the solar flare complex network. The limited predictability, nonlinearity, and self-organized criticality of the flares allow us to study systems of flares in the field of the complex systems. Both the occurrence time and the location of flares detected from 2006 January 1 to 2016 July 21 are used to design the growing flares network. The solar surface is divided into cells with equal areas. The cells, which include flares, are considered nodes of the network. The related links are equivalent to sympathetic flaring. The extracted features demonstrate that the network of flares follows quantitative measures of complexity. The power-law nature of the connectivity distribution with a degree exponent greater than three reveals that flares form a scale-free and small-world network. A large value for the clustering coefficient, a small characteristic path length, and a slow change of the diameter are all characteristics of the flares network. We show that the degree correlation of the flares network has the characteristics of a disassortative network. About 11% of the large energetic flares (M and X types in GOES classification) that occurred in the network hubs cover 3% of the solar surface.
Directory of Open Access Journals (Sweden)
Ernesto Michelangelo Giglio
2011-06-01
Full Text Available O artigo apresenta uma proposta e defesa da inclusão do ator consumidor nos raciocínios e pesquisas sobre redes, a partir da teoria das redes sociais. A proposta decorre da análise e reflexão sobre 82 artigos de redes selecionados, cujos objetivos incluíam o consumidor. Esta análise mostrou que o consumidor está ausente como ator, tanto teoricamente, quanto nas sugestões gerenciais. Seu papel na rede é secundário e são raros os estudos sobre a gestão de sua participação. Entre as causas dessa ausência, destacam-se a dominância de modelos sócio técnicos de redes na bibliografia e o uso de teorias da psicologia do indivíduo, quando se aborda o consumidor, o que se entende como inadequado num raciocínio de redes a partir das redes sociais. Nas conclusões, propõe-se um conjunto de princípios que inclui o consumidor como ator da rede, ampliando o campo de reflexões e de pesquisas da área. --- Proposed Reference Table to Include the Consumer in Network Concepts --- Abstract --- The article presents a model that includes the consumer in the principles and research on networks, using the concepts of social networks. The model arises from the analysis and reflections of 82 articles about networks, whose objectives included the consumer. It showed that he/she is absent as an actor in both theoretically and management proposals. His/her role in the network is secondary and there are few studies into the management of his/her participation. Among the causes of this absence we identify the dominance of socio-technical models in the bibliography and the use of theories of individual psychology, which are inadequate in a reasoning of social networks. Finally we propose a set of principles that includes the consumer as an actor in a network, widening the reflections and research in this area.
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Andy M Reynolds
2007-04-01
Full Text Available During their trajectories in still air, fruit flies (Drosophila melanogaster explore their landscape using a series of straight flight paths punctuated by rapid 90 degrees body-saccades [1]. Some saccades are triggered by visual expansion associated with collision avoidance. Yet many saccades are not triggered by visual cues, but rather appear spontaneously. Our analysis reveals that the control of these visually independent saccades and the flight intervals between them constitute an optimal scale-free active searching strategy. Two characteristics of mathematical optimality that are apparent during free-flight in Drosophila are inter-saccade interval lengths distributed according to an inverse square law, which does not vary across landscape scale, and 90 degrees saccade angles, which increase the likelihood that territory will be revisited and thereby reduce the likelihood that near-by targets will be missed. We also show that searching is intermittent, such that active searching phases randomly alternate with relocation phases. Behaviorally, this intermittency is reflected in frequently occurring short, slow speed inter-saccade intervals randomly alternating with rarer, longer, faster inter-saccade intervals. Searching patterns that scale similarly across orders of magnitude of length (i.e., scale-free have been revealed in animals as diverse as microzooplankton, bumblebees, albatrosses, and spider monkeys, but these do not appear to be optimised with respect to turning angle, whereas Drosophila free-flight search does. Also, intermittent searching patterns, such as those reported here for Drosophila, have been observed in foragers such as planktivorous fish and ground foraging birds. Our results with freely flying Drosophila may constitute the first reported example of searching behaviour that is both scale-free and intermittent.
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Kazuo eNakashima
2014-05-01
Full Text Available Drought negatively impacts plant growth and the productivity of crops around the world. Understanding the molecular mechanisms in the drought response is important for improvement of drought tolerance using molecular techniques. In plants, abscisic acid (ABA is accumulated under osmotic stress conditions caused by drought, and has a key role in stress responses and tolerance. Comprehensive molecular analyses have shown that ABA regulates the expression of many genes under osmotic stress conditions, and the ABA-responsive element (ABRE is the major cis-element for ABA-responsive gene expression. Transcription factors (TFs are master regulators of gene expression. ABRE-binding protein (AREB and ABRE-binding factor (ABF TFs control gene expression in an ABA-dependent manner. SNF1-related protein kinases 2, group A 2C-type protein phosphatases, and ABA receptors were shown to control the ABA signaling pathway. ABA-independent signaling pathways such as dehydration-responsive element-binding protein (DREB TFs and NAC TFs are also involved in stress responses including drought, heat and cold. Recent studies have suggested that there are interactions between the major ABA signaling pathway and other signaling factors in stress responses. The important roles of these transcription factors in crosstalk among abiotic stress responses will be discussed. Control of ABA or stress signaling factor expression can improve tolerance to environmental stresses. Recent studies using crops have shown that stress-specific overexpression of TFs improves drought tolerance and grain yield compared with controls in the field.
Reciprocity and the Emergence of Power Laws in Social Networks
Schnegg, Michael
Research in network science has shown that many naturally occurring and technologically constructed networks are scale free, that means a power law degree distribution emerges from a growth model in which each new node attaches to the existing network with a probability proportional to its number of links (= degree). Little is known about whether the same principles of local attachment and global properties apply to societies as well. Empirical evidence from six ethnographic case studies shows that complex social networks have significantly lower scaling exponents γ ~ 1 than have been assumed in the past. Apparently humans do not only look for the most prominent players to play with. Moreover cooperation in humans is characterized through reciprocity, the tendency to give to those from whom one has received in the past. Both variables — reciprocity and the scaling exponent — are negatively correlated (r = -0.767, sig = 0.075). If we include this effect in simulations of growing networks, degree distributions emerge that are much closer to those empirically observed. While the proportion of nodes with small degrees decreases drastically as we introduce reciprocity, the scaling exponent is more robust and changes only when a relatively large proportion of attachment decisions follow this rule. If social networks are less scale free than previously assumed this has far reaching implications for policy makers, public health programs and marketing alike.
Energy Technology Data Exchange (ETDEWEB)
Kuhlemann, Verena [Emory Univ., Atlanta, GA (United States); Vassilevski, Panayot S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2013-10-28
Matrix-vector multiplication is the key operation in any Krylov-subspace iteration method. We are interested in Krylov methods applied to problems associated with the graph Laplacian arising from large scale-free graphs. Furthermore, computations with graphs of this type on parallel distributed-memory computers are challenging. This is due to the fact that scale-free graphs have a degree distribution that follows a power law, and currently available graph partitioners are not efficient for such an irregular degree distribution. The lack of a good partitioning leads to excessive interprocessor communication requirements during every matrix-vector product. Here, we present an approach to alleviate this problem based on embedding the original irregular graph into a more regular one by disaggregating (splitting up) vertices in the original graph. The matrix-vector operations for the original graph are performed via a factored triple matrix-vector product involving the embedding graph. And even though the latter graph is larger, we are able to decrease the communication requirements considerably and improve the performance of the matrix-vector product.
Exploring network operations for data and information networks
Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming
2017-01-01
Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.
Tala-Tebue, E.; Tsobgni-Fozap, D. C.; Kenfack-Jiotsa, A.; Kofane, T. C.
2014-06-01
Using the Jacobi elliptic functions and the alternative ( G'/ G-expansion method including the generalized Riccati equation, we derive exact soliton solutions for a discrete nonlinear electrical transmission line in (2+1) dimension. More precisely, these methods are general as they lead us to diverse solutions that have not been previously obtained for the nonlinear electrical transmission lines. This study seeks to show that it is not often necessary to transform the equation of the network into a well-known differential equation before finding its solutions. The solutions obtained by the current methods are generalized periodic solutions of nonlinear equations. The shape of solutions can be well controlled by adjusting the parameters of the network. These exact solutions may have significant applications in telecommunication systems where solitons are used to codify or for the transmission of data.
Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.
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Mahendra Piraveenan
Full Text Available A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.
Energy Technology Data Exchange (ETDEWEB)
Fazlali, Alireza; Koranian, Parvaneh [Arak University, Arak (Iran, Islamic Republic of); Beigzadeh, Reza [Islamic Azad University, Kermanshah (Iran, Islamic Republic of); Rahimi, Masoud [Razi University, Kermanshah (Iran, Islamic Republic of)
2013-09-15
A feed forward three-layer artificial neural network (ANN) model was developed for VLE prediction of ternary systems including ionic liquid (IL) (water+ethanol+1-butyl-3- methyl-imidazolium acetate), in a relatively wide range of IL mass fractions up to 0.8, with the mole fractions of ethanol on IL-free basis fixed separately at 0.1, 0.2, 0.4, 0.6, 0.8, and 0.98. The output results of the ANN were the mole fraction of ethanol in vapor phase and the equilibrium temperature. The validity of the model was evaluated through a test data set, which were not employed in the training case of the network. The performance of the ANN model for estimating the mole fraction and temperature in the ternary system including IL was compared with the non-random-two-liquid (NRTL) and electrolyte non-random-two- liquid (eNRTL) models. The results of this comparison show that the ANN model has a superior performance in predicting the VLE of ternary systems including ionic liquid.
The emergent properties of a dolphin social network.
Lusseau, David
2003-11-07
Many complex networks, including human societies, the Internet, the World Wide Web and power grids, have surprising properties that allow vertices (individuals, nodes, Web pages, etc.) to be in close contact and information to be transferred quickly between them. Nothing is known of the emerging properties of animal societies, but it would be expected that similar trends would emerge from the topology of animal social networks. Despite its small size (64 individuals), the Doubtful Sound community of bottlenose dolphins has the same characteristics. The connectivity of individuals follows a complex distribution that has a scale-free power-law distribution for large k. In addition, the ability for two individuals to be in contact is unaffected by the random removal of individuals. The removal of individuals with many links to others does affect the length of the 'information' path between two individuals, but, unlike other scale-free networks, it does not fragment the cohesion of the social network. These self-organizing phenomena allow the network to remain united, even in the case of catastrophic death events.
Akin, Ibrahim; Schneider, Henrik; Nienaber, Christoph A; Jung, Werner; Lübke, Mike; Rillig, Andreas; Ansari, Uzair; Wunderlich, Nina; Birkemeyer, Ralf
2015-07-11
Studies have associated obesity with better outcomes in comparison to non-obese patients after elective and emergency coronary revascularization. However, these findings might have been influenced by patient selection. Therefore we thought to look into the obesity paradox in a consecutive network STEMI population. The database of two German myocardial infarction network registries were combined and data from a total of 890 consecutive patients admitted and treated for acute STEMI including cardiogenic shock and cardiopulmonary resuscitation according to standardized protocols were analyzed. Patients were categorized in normal weight (≤24.9 kg/m(2)), overweight (25-30 kg/m(2)) and obese (>30 kg/m(2)) according to BMI. Baseline clinical parameters revealed a higher comorbidity index for overweight and obese patients; 1-year follow-up comparison between varying groups revealed similar rates of all-cause death (9.1 % vs. 8.3 % vs. 6.2 %; p = 0.50), major adverse cardiac and cerebrovascular [MACCE (15.1 % vs. 13.4 % vs. 10.2 %; p = 0.53)] and target vessel revascularization in survivors [TVR (7.0 % vs. 5.0 % vs. 4.0 %; p = 0.47)] with normal weight when compared to overweight or obese patients. These results persisted after risk-adjustment for heterogeneous baseline characteristics of groups. An analysis of patients suffering from cardiogenic shock showed no impact of BMI on clinical endpoints. Our data from two network systems in Germany revealed no evidence of an "obesity paradox"in an all-comer STEMI population including patients with cardiogenic shock.
Energy Technology Data Exchange (ETDEWEB)
Rasgado Casique, Jose Pepe; Silva Farias, Jose Luis [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico)]. E-mail: jrasgado@iie.org.mx; jlsilva@iie.org.mx
2010-11-15
This article presents a methodology for conducting electrical studies in industrial networks. The methodology included the study of arc flash as a very important area of current basic electrical studies, such as power flow, short circuit and coordination. The aim of this study is to determine the Personal Protective Equipment (PPE) and flash protection boundary for personnel working with or near energized equipment, based on the IEEE Std 1584-2004 and NFPA-70E- 2004. Also included are criteria and recommendations to reduce incident energy level (cal/cm{sup 2}). At work we used a distribution network for industrial type test. The studies were carried out using a commercial program for the analysis of electrical networks. [Spanish] En este articulo se presenta una metodologia para llevar a cabo los estudios electricos en redes industriales. En la metodologia se incluye al estudio de arco electrico como un area muy importante de los estudios electricos basicos actuales, como: flujos de potencia, cortocircuito y coordinacion de protecciones. El objetivo de dicho estudio es determinar el Equipo de Proteccion Personal (EPP) apropiado y los limites de proteccion para el personal que opera con o cerca de equipo energizado, con base en las normas IEEE Std. 1584-2004 y la NFPA-70E-2004. Ademas, se incluyen criterios y recomendaciones para disminuir el nivel de energia incidente (cal/cm{sup 2}). En el trabajo se utilizo una red de distribucion tipo industrial de prueba. Los estudios se llevaron a cabo utilizando un programa comercial para el analisis de redes electricas.
Faibisoff, Sylvia G.
This report reviews the activities, structure, and organization of the Southwest Michigan Library Network (SMLN) and provides a review of multitype networking in several other states, sources of funding, and issues in national networking. The SMLN is a cooperative group of 56 libraries located within the five counties of Allegan, Berrien, Cass,…
Generalized synchronization in complex dynamical networks via adaptive couplings
Liu, Hui; Chen, Juan; Lu, Jun-an; Cao, Ming
2010-01-01
This paper investigates generalized synchronization of three typical classes of complex dynamical networks: scale-free networks, small-world networks. and interpolating networks. The proposed synchronization strategy is to adjust adaptively a node's coupling strength based oil the node's local
Robustness and structure of complex networks
Shao, Shuai
This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks
Brain scale-free properties in awake rest and NREM sleep: a simultaneous EEG/fMRI study.
Lei, Xu; Wang, Yulin; Yuan, Hong; Chen, Antao
2015-03-01
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies revealed that spontaneous activity in the brain has scale-invariant properties, as indicated by a frequency spectrum that follows a power-law distribution. However, current knowledge about the exact relationship between scaling properties in EEG and fMRI signals is very limited. To address this question, we collected simultaneous EEG-fMRI data in healthy individuals during resting wakefulness and non-rapid eye movement (NREM) sleep. For either of these conditions, we found that both EEG and fMRI power spectra followed a power-law distribution. Furthermore, the EEG and fMRI scaling exponents were highly variable across subjects, and sensitive to the choice of reference and nuisance variables in EEG and fMRI data, respectively. Interestingly, the EEG exponent of the whole brain selectively corresponded to the fMRI exponent of the thalamus during NREM sleep. Together, our findings suggest that scale-free brain activity is characterized by robust temporal structures and behavioral significance. This motivates future studies to unravel its physiological mechanisms, as well as its relevance to behavior.
Directory of Open Access Journals (Sweden)
Deussing Jan M
2010-10-01
Full Text Available Abstract Background The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. Results Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN, 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2 and Amyloid β (A4 precursor protein (APP were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. Conclusions This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.
The Evolutionary Vaccination Dilemma in Complex Networks
Cardillo, Alessio; Naranjo, Fernando; Gómez-Gardeñes, Jesús
2013-01-01
In this work we analyze the evolution of voluntary vaccination in networked populations by entangling the spreading dynamics of an influenza-like disease with an evolutionary framework taking place at the end of each influenza season so that individuals take or not the vaccine upon their previous experience. Our framework thus put in competition two well-known dynamical properties of scale-free networks: the fast propagation of diseases and the promotion of cooperative behaviours. Our results show that when vaccine is perfect scale-free networks enhance the vaccination behaviour with respect to random graphs with homogeneous connectivity patterns. However, when imperfection appears we find a cross-over effect so that the number of infected (vaccinated) individuals increases (decreases) with respect to homogeneous networks, thus showing up the competition between the aforementioned properties of scale-free graphs.
Coherence in Complex Networks of Oscillators
Lind, Pedro G.; Gallas, Jason A. C.; Herrmann, Hans J.
We study fully synchronized (coherent) states in complex networks of chaotic oscillators, reviewing the analytical approach of determining the stability conditions for synchronizability and comparing them with numerical criteria. As an example, we present detailed results for networks of chaotic logistic maps having three different scale-free topologies: random scale-free topology, deterministic pseudo-fractal scale-free network and Apollonian network. For random scale-free topology we find that the lower boundary of the synchronizability region scales approximately as k-μ, where k is the outgoing connectivity and μ depends on the local nonlinearity. For deterministic scale-free networks coherence is observed only when the coupling is heterogeneous, namely when it is proportional to some power of the neighbour connectivity. In all cases, stability conditions are determined from the eigenvalue spectrum of the Laplacian matrix and agree well with numerical results based on histograms of coherent states in parameter space. Additionally, we show that almost everywhere in the synchronizability region the basin of attraction of the coherent states fills the entire phase space, and that the transition to coherence is of first-order.
DEFF Research Database (Denmark)
Brewka, Lukasz Jerzy; Gavler, Anders; Wessing, Henrik
2012-01-01
End-to-end quality of service provisioning is still a challenging task despite many years of research and development in this area. Considering a generalized multi-protocol label switching based core/metro network and resource reservation protocol capable home gateways, it is the access part...... of the network where quality of service signaling is bridged. This article proposes strategies for generalized multi-protocol label switching control over next emerging passive optical network standard, i.e., the 10-gigabit-capable passive optical network. Node management and resource allocation approaches...... are discussed, and possible issues are raised. The analysis shows that consideration of a 10-gigabit-capable passive optical network as a generalized multi-protocol label switching controlled domain is valid and may advance end-to-end quality of service provisioning for passive optical network based customers....
Adaptive bridge control strategy for opinion evolution on social networks.
Qian, Cheng; Cao, Jinde; Lu, Jianquan; Kurths, Jürgen
2011-06-01
In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes' degree and other global∕local network structure information, is proposed to control the "bridges" more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.
Unemo, Magnus; Ison, Catherine A; Cole, Michelle; Spiteri, Gianfranco; van de Laar, Marita; Khotenashvili, Lali
2013-12-01
Antimicrobial resistance (AMR) in Neisseria gonorrhoeae has emerged for essentially all antimicrobials following their introduction into clinical practice. During the latest decade, susceptibility to the last remaining options for antimicrobial monotherapy, the extended-spectrum cephalosporins (ESC), has markedly decreased internationally and treatment failures with these ESCs have been verified. In response to this developing situation, WHO and the European Centre for Disease Prevention and Control (ECDC) have published global and region-specific response plans, respectively. One main component of these action/response plans is to enhance the surveillance of AMR and treatment failures. This paper describes the perspectives from the diverse WHO European Region (53 countries), including the independent countries of the former Soviet Union, regarding gonococcal AMR surveillance networks. The WHO European Region has a high prevalence of resistance to all previously recommended antimicrobials, and most of the first strictly verified treatment failures with cefixime and ceftriaxone were also reported from Europe. In the European Union/European Economic Area (EU/EEA), the European gonococcal antimicrobial surveillance programme (Euro-GASP) funded by the ECDC is running. In 2011, the Euro-GASP included 21/31 (68%) EU/EEA countries, and the programme is further strengthened annually. However, in the non-EU/EEA countries, internationally reported and quality assured gonococcal AMR data are lacking in 87% of the countries and, worryingly, appropriate support for establishment of a GASP is still lacking. Accordingly, national and international support, including political and financial commitment, for gonococcal AMR surveillance in the non-EU/EEA countries of the WHO European Region is essential.
Molecular ecological network analyses
Directory of Open Access Journals (Sweden)
Deng Ye
2012-05-01
Full Text Available Abstract Background Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs through Random Matrix Theory (RMT-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological
General scaling of maximum degree of synchronization in noisy complex networks
Traxl, Dominik; Boers, Niklas; Kurths, Jürgen
2014-11-01
The effects of white noise and global coupling strength on the maximum degree of synchronization in complex networks are explored. We perform numerical simulations of generic oscillator models with both linear and non-linear coupling functions on a broad spectrum of network topologies. The oscillator models include the Fitzhugh-Nagumo model, the Izhikevich model and the Kuramoto phase oscillator model. The network topologies range from regular, random and highly modular networks to scale-free and small-world networks, with both directed and undirected edges. We then study the dependency of the maximum degree of synchronization on the global coupling strength and the noise intensity. We find a general scaling of the synchronizability, and quantify its validity by fitting a regression model to the numerical data.
Zhou, Tao
2008-05-01
In this article, we propose a mixing navigation mechanism, which interpolates between random-walk and shortest-path protocol. The navigation efficiency can be remarkably enhanced via a few routers. Some advanced strategies are also designed: For non-geographical scale-free networks, the targeted strategy with a tiny fraction of routers can guarantee an efficient navigation with low and stable delivery time almost independent of network size. For geographical localized networks, the clustering strategy can simultaneously increase efficiency and reduce the communication cost. The present mixing navigation mechanism is of significance especially for information organization of wireless sensor networks and distributed autonomous robotic systems.
Modern network science of neurological disorders
Stam, C.J.
2014-01-01
Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in
Biased trapping issue on weighted hierarchical networks
Indian Academy of Sciences (India)
In this paper, we present trapping issues of weight-dependent walks on weighted hierarchical networks which are based on the classic scale-free hierarchical networks. Assuming that edge's weight is used as local information by a random walker, we introduce a biased walk. The biased walk is that a walker, at each step, ...
False Positive and False Negative Effects on Network Attacks
Shang, Yilun
2017-11-01
Robustness against attacks serves as evidence for complex network structures and failure mechanisms that lie behind them. Most often, due to detection capability limitation or good disguises, attacks on networks are subject to false positives and false negatives, meaning that functional nodes may be falsely regarded as compromised by the attacker and vice versa. In this work, we initiate a study of false positive/negative effects on network robustness against three fundamental types of attack strategies, namely, random attacks (RA), localized attacks (LA), and targeted attack (TA). By developing a general mathematical framework based upon the percolation model, we investigate analytically and by numerical simulations of attack robustness with false positive/negative rate (FPR/FNR) on three benchmark models including Erdős-Rényi (ER) networks, random regular (RR) networks, and scale-free (SF) networks. We show that ER networks are equivalently robust against RA and LA only when FPR equals zero or the initial network is intact. We find several interesting crossovers in RR and SF networks when FPR is taken into consideration. By defining the cost of attack, we observe diminishing marginal attack efficiency for RA, LA, and TA. Our finding highlights the potential risk of underestimating or ignoring FPR in understanding attack robustness. The results may provide insights into ways of enhancing robustness of network architecture and improve the level of protection of critical infrastructures.
Eradicating catastrophic collapse in interdependent networks via reinforced nodes.
Yuan, Xin; Hu, Yanqing; Stanley, H Eugene; Havlin, Shlomo
2017-03-28
In interdependent networks, it is usually assumed, based on percolation theory, that nodes become nonfunctional if they lose connection to the network giant component. However, in reality, some nodes, equipped with alternative resources, together with their connected neighbors can still be functioning after disconnected from the giant component. Here, we propose and study a generalized percolation model that introduces a fraction of reinforced nodes in the interdependent networks that can function and support their neighborhood. We analyze, both analytically and via simulations, the order parameter-the functioning component-comprising both the giant component and smaller components that include at least one reinforced node. Remarkably, it is found that, for interdependent networks, we need to reinforce only a small fraction of nodes to prevent abrupt catastrophic collapses. Moreover, we find that the universal upper bound of this fraction is 0.1756 for two interdependent Erdős-Rényi (ER) networks: regular random (RR) networks and scale-free (SF) networks with large average degrees. We also generalize our theory to interdependent networks of networks (NONs). These findings might yield insight for designing resilient interdependent infrastructure networks.
False Positive and False Negative Effects on Network Attacks
Shang, Yilun
2018-01-01
Robustness against attacks serves as evidence for complex network structures and failure mechanisms that lie behind them. Most often, due to detection capability limitation or good disguises, attacks on networks are subject to false positives and false negatives, meaning that functional nodes may be falsely regarded as compromised by the attacker and vice versa. In this work, we initiate a study of false positive/negative effects on network robustness against three fundamental types of attack strategies, namely, random attacks (RA), localized attacks (LA), and targeted attack (TA). By developing a general mathematical framework based upon the percolation model, we investigate analytically and by numerical simulations of attack robustness with false positive/negative rate (FPR/FNR) on three benchmark models including Erdős-Rényi (ER) networks, random regular (RR) networks, and scale-free (SF) networks. We show that ER networks are equivalently robust against RA and LA only when FPR equals zero or the initial network is intact. We find several interesting crossovers in RR and SF networks when FPR is taken into consideration. By defining the cost of attack, we observe diminishing marginal attack efficiency for RA, LA, and TA. Our finding highlights the potential risk of underestimating or ignoring FPR in understanding attack robustness. The results may provide insights into ways of enhancing robustness of network architecture and improve the level of protection of critical infrastructures.
Opinion formation of free speech on the directed social network
Su, Jiongming; Ma, Hongxu; Liu, Baohong; Li, Qi
2014-12-01
A dynamical model with continuous opinion is proposed to study how the speech order and the topology of directed social network affect the opinion formation of free speech. In the model, agents express their opinions one by one with random order (RO) or probability order (PO), other agents paying attentions to the speaking agent, receive provider's opinion, update their opinions and then express their new opinions in their turns. It is proved that with the same agent j repeats its opinion more, other agents who pay their attentions to j and include j's opinion in their confidence level at initial time, will continue approaching j's opinion. Simulation results reveal that on directed scale-free network: (1) the model for PO forms fewer opinion clusters, larger maximum cluster (MC), smaller standard deviation (SD), and needs less waiting time to reach a middle level of consensus than RO; (2) as the parameter of scale-free degree distribution decreases or the confidence level increases, the results often get better for both speech orders; (3) the differences between PO and RO get smaller as the size of network decreases.
U.S. Environmental Protection Agency — The INTERSTATES layer contains the Interstate Highway network, using NAVTEQ Functional Class=1 for United States and Canada. This 5 layer SDC dataset represents a...
U.S. Environmental Protection Agency — The MROADS layer contains the Major Roads network using NAVTEQ Functional Class=1,2,3,4, where 4 represents routes connecting minor towns or villages and collecting...
Asnaashari, Maryam; Farhoosh, Reza; Farahmandfar, Reza
2016-10-01
As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR). The results showed ANFIS was the best model with high coefficient of determination (R(2) = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models. Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
Li, Jianan; Zhou, Qizhi; Campos, Luiza C
2017-12-01
Greater duckweed (Spirodela polyrhiza) based lab-scale free water constructed wetland (CW) was employed for removing four emerging pharmaceuticals and personal care products (PPCPs) (i.e. DEET, paracetamol, caffeine and triclosan). Orthogonal design was used to test the effect of light intensity, aeration, E.coli abundance and plant biomass on the target compounds. Synthetic wastewater contaminated with the target compounds at concentration of 25 μg/L was prepared, and both batch and continuous flow experiments were conducted. Up to 100% removals were achieved for paracetamol (PAR), caffeine (CAF) and tricolsan (TCS) while the highest removal for DEET was 32.2% in batch tests. Based on orthogonal Duncan analysis, high light intensity (240 μmolmm -2 s -1 ), full aeration, high plant biomass (1.00 kg/m 2 ) and high E.coli abundance (1.0 × 10 6 CFU/100 mL) favoured elimination of the PPCPs. Batch verification test achieved removals of 17.1%, 98.8%, 96.4% and 95.4% for DEET, PAR, CAF and TCS respectively. Continuous flow tests with CW only and CW followed by stabilization tank (CW-ST) were carried out. Final removals of the PPCP contaminants were 32.6%, 97.7%, 98.0% and 100% for DEET, PAR, CAF and TCS, respectively, by CW system alone, while 43.3%, 97.5%, 98.2% and 100%, respectively, were achieved by CW-ST system. By adding the ST tank, PPCP concentrations decreased significantly faster (p < 0.05) compared with continuous flow CW alone. In addition, after removing aerators during continuous flow CW experiments, the treatment systems presented good stability for the PPCP removals. CW-ST showed better chemical oxygen demand (COD) and total organic carbon (TOC) removals (89.3%, 91.2%, respectively) than CW only (79.4%, 85.2%, respectively). However, poor DEET removal (<50%) and high E.coli abundance (up to 1.7 log increase) in the final treated water indicated further treatment processes may be required. Statistical analysis showed significant correlations
Fitness for synchronization of network motifs
DEFF Research Database (Denmark)
Vega, Y.M.; Vázquez-Prada, M.; Pacheco, A.F.
2004-01-01
We study the synchronization of Kuramoto's oscillators in small parts of networks known as motifs. We first report on the system dynamics for the case of a scale-free network and show the existence of a non-trivial critical point. We compute the probability that network motifs synchronize, and fi...... that the fitness for synchronization correlates well with motifs interconnectedness and structural complexity. Possible implications for present debates about network evolution in biological and other systems are discussed....
National Research Council Canada - National Science Library
Ochab, Jeremi K; Tyburczyk, Jacek; Beldzik, Ewa; Chialvo, Dante R; Domagalik, Aleksandra; Fafrowicz, Magdalena; Gudowska-Nowak, Ewa; Marek, Tadeusz; Nowak, Maciej A; Oginska, Halszka; Szwed, Jerzy
2014-01-01
The timing and dynamics of many diverse behaviors of mammals, e.g., patterns of animal foraging or human communication in social networks exhibit complex self-similar properties reproducible over multiple time scales...
Grassilli, Silvia; Nika, Ervin; Lambertini, Elisabetta; Brugnoli, Federica; Piva, Roberta; Capitani, Silvano; Bertagnolo, Valeria
2016-10-01
Reduced expression of miR-142-3p has been found to be associated with the development of various subtypes of myeloid leukemia, including acute promyelocytic leukemia (APL). In APL-derived cells, miR-142-3p expression can be restored by all-trans retinoic acid (ATRA), which induces the completion of their maturation program. Here, we aimed to assess whether PU.1, essential for ATRA-induced gene transcription, regulates the expression of miR-142-3p in APL-derived cells and, based on the established cooperation between PU.1 and Vav1 in modulating gene expression, to evaluate the role of Vav1 in restoring the expression of miR-142-3p. ATRA-induced increases in PU.1 and Vav1 expression in APL-derived NB4 cells were counteracted with specific siRNAs, and the expression of miR-142-3p was measured by quantitative real-time PCR (qRT-PCR). The recruitment of PU.1 and/or Vav1 to the regulatory region of miR-142 was assessed by quantitative chromatin immunoprecipitation (Q-ChIP). Synthetic inhibitors or mimics for miR-142-3p were used to assess whether this miRNA plays a role in regulating the expression of PU.1 and/or Vav1. We found that the expression of miR-142-3p in differentiating APL-derived NB4 cells is dependent on PU.1, and that Vav1 is essential for the recruitment of this transcription factor to its cis-binding element on the miR-142 promoter. In addition, we found that in ATRA-treated NB4 cells miR-142-3p sustains agonist-induced increases in both PU.1 and Vav1. Our results suggest the existence of a Vav1/PU.1/miR-142-3p network that supports ATRA-induced differentiation in APL-derived cells. Since selective regulation of miRNAs may play a role in the future treatment of hematopoietic malignancies, our results may provide a basis for the development of new therapeutic strategies to restore the expression of miR-142-3p.
A Model for Improving the Learning Curves of Artificial Neural Networks.
Directory of Open Access Journals (Sweden)
Roberto L S Monteiro
Full Text Available In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.
Controllability of giant connected components in a directed network
Liu, Xueming; Pan, Linqiang; Stanley, H. Eugene; Gao, Jianxi
2017-04-01
When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p =pm . We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree and that it is determined by pm . In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.
From topology to dynamics in biochemical networks.
Fox, Jeffrey J.; Hill, Colin C.
2001-12-01
Abstract formulations of the regulation of gene expression as random Boolean switching networks have been studied extensively over the past three decades. These models have been developed to make statistical predictions of the types of dynamics observed in biological networks based on network topology and interaction bias, p. For values of mean connectivity chosen to correspond to real biological networks, these models predict disordered dynamics. However, chaotic dynamics seems to be absent from the functioning of a normal cell. While these models use a fixed number of inputs for each element in the network, recent experimental evidence suggests that several biological networks have distributions in connectivity. We therefore study randomly constructed Boolean networks with distributions in the number of inputs, K, to each element. We study three distributions: delta function, Poisson, and power law (scale free). We analytically show that the critical value of the interaction bias parameter, p, above which steady state behavior is observed, is independent of the distribution in the limit of the number of elements N--> infinity. We also study these networks numerically. Using three different measures (types of attractors, fraction of elements that are active, and length of period), we show that finite, scale-free networks are more ordered than either the Poisson or delta function networks below the critical point. Thus the topology of scale-free biochemical networks, characterized by a wide distribution in the number of inputs per element, may provide a source of order in living cells. (c) 2001 American Institute of Physics.
Energy Technology Data Exchange (ETDEWEB)
Hernandez-Bermejo, B. [Departamento de Fisica, Universidad Rey Juan Carlos, Escuela Superior de Ciencias Experimentales y Tecnologia, Edificio Departamental II, Calle Tulipan S/N, 28933-Mostoles-Madrid (Spain)], E-mail: benito.hernandez@urjc.es; Marco-Blanco, J. [Departamento de Fisica, Universidad Rey Juan Carlos, Escuela Superior de Ciencias Experimentales y Tecnologia, Edificio Departamental II, Calle Tulipan S/N, 28933-Mostoles-Madrid (Spain); Romance, M. [Departamento de Matematica Aplicada, Universidad Rey Juan Carlos, Escuela Superior de Ciencias Experimentales y Tecnologia, Edificio Departamental II, Calle Tulipan S/N, 28933-Mostoles-Madrid (Spain)
2009-02-23
Estimates for the efficiency of a tree are derived, leading to new analytical expressions for Barabasi-Albert trees efficiency. These expressions are used to investigate the dynamic behaviour of such networks. It is proved that the preferential attachment leads to an asymptotic conservation of efficiency as the Barabasi-Albert trees grow.
Disease Surveillance on Complex Social Networks.
Directory of Open Access Journals (Sweden)
Jose L Herrera
2016-07-01
Full Text Available As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors-sampling the most connected, random, and friends of random individuals-in three complex social networks-a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals-early and accurate detection of epidemic emergence and peak, and general situational awareness-we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0. For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.
Systemic risk on different interbank network topologies
Lenzu, Simone; Tedeschi, Gabriele
2012-09-01
In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents' performance. By changing the agent's trust on its neighbor's performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents' heterogeneity.
Systematic network assessment of the carcinogenic activities of cadmium.
Chen, Peizhan; Duan, Xiaohua; Li, Mian; Huang, Chao; Li, Jingquan; Chu, Ruiai; Ying, Hao; Song, Haiyun; Jia, Xudong; Ba, Qian; Wang, Hui
2016-11-01
Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. Copyright © 2016 Elsevier Inc. All rights reserved.
The complex network reliability and influential nodes
Li, Kai; He, Yongfeng
2017-08-01
In order to study the complex network node important degree and reliability, considering semi-local centrality, betweenness centrality and PageRank algorithm, through the simulation method to gradually remove nodes and recalculate the importance in the random network, small world network and scale-free network. Study the relationship between the largest connected component and node removed proportion, the research results show that betweenness centrality and PageRank algorithm based on the global information network are more effective for evaluating the importance of nodes, and the reliability of the network is related to the network topology.
Identifying Vulnerable Nodes of Complex Networks in Cascading Failures Induced by Node-Based Attacks
Directory of Open Access Journals (Sweden)
Shudong Li
2013-01-01
Full Text Available In the research on network security, distinguishing the vulnerable components of networks is very important for protecting infrastructures systems. Here, we probe how to identify the vulnerable nodes of complex networks in cascading failures, which was ignored before. Concerned with random attack (RA and highest load attack (HL on nodes, we model cascading dynamics of complex networks. Then, we introduce four kinds of weighting methods to characterize the nodes of networks including Barabási-Albert scale-free networks (SF, Watts-Strogatz small-world networks (WS, Erdos-Renyi random networks (ER, and two real-world networks. The simulations show that, for SF networks under HL attack, the nodes with small value of the fourth kind of weight are the most vulnerable and the ones with small value of the third weight are also vulnerable. Also, the real-world autonomous system with power-law distribution verifies these findings. Moreover, for WS and ER networks under both RA and HL attack, when the nodes have low tolerant ability, the ones with small value of the fourth kind of weight are more vulnerable and also the ones with high degree are easier to break down. The results give us important theoretical basis for digging the potential safety loophole and making protection strategy.
Cooperation on Social Networks and Its Robustness
ALBERTO ANTONIONI; MARCO TOMASSINI
2012-01-01
In this work we have used computer models of social-like networks to show by extensive numerical simulations that cooperation in evolutionary games can emerge and be stable on this class of networks. The amounts of cooperation reached are at least as much as in scale-free networks but here the population model is more realistic. Cooperation is robust with respect to different strategy update rules, population dynamics, and payoff computation. Only when straight average payoff is used or there...
Extraction of network topology from multi-electrode recordings: Is there a small-world effect?
Directory of Open Access Journals (Sweden)
Felipe eGerhard
2011-02-01
Full Text Available The simultaneous recording of the activity of many neurons poses challenges for multivariate data analysis. Here, we propose a general scheme of reconstruction of the functional network from spike train recordings. Effective, causal interactions are estimated by fitting Generalized Linear Models (GLMs on the neural responses, incorporating effects of the neurons' self-history, of input from other neurons in the recorded network and of modulation by an external stimulus. The coupling terms arising from synaptic input can be transformed by thresholding into a binary connectivity matrix which is directed. Each link between two neurons represents a causal influence from one neuron to the other, given the observation of all other neurons from the population. The resulting graph is analyzed with respect to small-world and scale-free properties using quantitative measures for directed networks. Such graph-theoretic analyses have been performed on many complex dynamic networks, including the connectivity structure between different brain areas. Only few studies have attempted to look at the structure of cortical neural networks on the level of individual neurons. Here, using multi-electrode recordings from the visual system of the awake monkey, we find that cortical networks lack scale-free behavior, but show a small, but significant small-world structure. Assuming a simple distance-dependent probabilistic wiring between neurons, we find that this connectivity structure can account for all of the networks' observed small-world-ness. Moreover, for multi-electrode recordings the sampling of neurons is not uniform across the population. We show that the small-world-ness obtained by such a localized sub-sampling overestimates the strength of the true small-world-structure of the network. This bias is likely to be present in all previous experiments based on multi-electrode recordings.
Network theory and its applications in economic systems
Huang, Xuqing
This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. 2) We analytically demonstrates that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the
Sentiment contagion in complex networks
Zhao, Laijun; Wang, Jiajia; Huang, Rongbing; Cui, Hongxin; Qiu, Xiaoyan; Wang, Xiaoli
2014-01-01
Sentiment contagion such as the spread of panic in emergencies is a common phenomenon in human society. Considering the difference between sentiment contagion and epidemic contagion, we define the transition probabilities of the binary emotional state (optimism, pessimism) and establish a sentiment contagion model. Transition equations are given in a homogeneous network and the stability of the zero solution is discussed. Also the Monte Carlo method is used for numerical simulation in the inhomogeneous networks. Simulation results show that the overall tendency of sentiment variation in the BA scale-free network is similar in the homogeneous network. Furthermore, the assimilation and weight combination exert influences on sentiment contagion.
Directory of Open Access Journals (Sweden)
Brenton J Prettejohn
2011-03-01
Full Text Available Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erd"{o}s-R'{e}nyi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the `scale-free' and `small-world' properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.
Improved Degree Search Algorithms in Unstructured P2P Networks
Directory of Open Access Journals (Sweden)
Guole Liu
2012-01-01
Full Text Available Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P networks. Breadth-first search (BFS and depth-first search (DFS are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD and memory function preference degree algorithm (PD. We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.
Directory of Open Access Journals (Sweden)
Korf Ulrike
2011-07-01
Full Text Available Abstract Background Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. Results We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. Conclusions The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.
Emergence of Scaling in Random Networks
Barabási, Albert-László; Albert, Réka
1999-10-01
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim
2014-12-03
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Research of Innovation Diffusion on Industrial Networks
Directory of Open Access Journals (Sweden)
Yongtai Chen
2014-01-01
Full Text Available The real value of innovation consists in its diffusion on industrial network. The factors which affect the diffusion of innovation on industrial network are the topology of industrial network and rules of diffusion. Industrial network is a complex network which has scale-free and small-world characters; its structure has some affection on threshold, length of path, enterprise’s status, and information share of innovation diffusion. Based on the cost and attitude to risk of technical innovation, we present the “avalanche” diffusing model of technical innovation on industrial network.
Ochab, Jeremi K; Tyburczyk, Jacek; Beldzik, Ewa; Chialvo, Dante R; Domagalik, Aleksandra; Fafrowicz, Magdalena; Gudowska-Nowak, Ewa; Marek, Tadeusz; Nowak, Maciej A; Oginska, Halszka; Szwed, Jerzy
2014-01-01
The timing and dynamics of many diverse behaviors of mammals, e.g., patterns of animal foraging or human communication in social networks exhibit complex self-similar properties reproducible over multiple time scales. In this paper, we analyze spontaneous locomotor activity of healthy individuals recorded in two different conditions: during a week of regular sleep and a week of chronic partial sleep deprivation. After separating activity from rest with a pre-defined activity threshold, we have detected distinct statistical features of duration times of these two states. The cumulative distributions of activity periods follow a stretched exponential shape, and remain similar for both control and sleep deprived individuals. In contrast, rest periods, which follow power-law statistics over two orders of magnitude, have significantly distinct distributions for these two groups and the difference emerges already after the first night of shortened sleep. We have found steeper distributions for sleep deprived individuals, which indicates fewer long rest periods and more turbulent behavior. This separation of power-law exponents is the main result of our investigations, and might constitute an objective measure demonstrating the severity of sleep deprivation and the effects of sleep disorders.
Directory of Open Access Journals (Sweden)
Jeremi K Ochab
Full Text Available The timing and dynamics of many diverse behaviors of mammals, e.g., patterns of animal foraging or human communication in social networks exhibit complex self-similar properties reproducible over multiple time scales. In this paper, we analyze spontaneous locomotor activity of healthy individuals recorded in two different conditions: during a week of regular sleep and a week of chronic partial sleep deprivation. After separating activity from rest with a pre-defined activity threshold, we have detected distinct statistical features of duration times of these two states. The cumulative distributions of activity periods follow a stretched exponential shape, and remain similar for both control and sleep deprived individuals. In contrast, rest periods, which follow power-law statistics over two orders of magnitude, have significantly distinct distributions for these two groups and the difference emerges already after the first night of shortened sleep. We have found steeper distributions for sleep deprived individuals, which indicates fewer long rest periods and more turbulent behavior. This separation of power-law exponents is the main result of our investigations, and might constitute an objective measure demonstrating the severity of sleep deprivation and the effects of sleep disorders.
Computing assortative mixing by degree with the s-metric in networks using linear programming
Waldorp, L.J.; Schmittmann, V.D.
2015-01-01
Calculation of assortative mixing by degree in networks indicates whether nodes with similar degree are connected to each other. In networks with scale-free distribution high values of assortative mixing by degree can be an indication of a hub-like core in networks. Degree correlation has generally
Roost networks of northern myotis (Myotis septentrionalis) in a managed landscape
Johnson, J.B.; Mark, Ford W.; Edwards, J.W.
2012-01-01
Maternity groups of many bat species conform to fission-fusion models and movements among diurnal roost trees and individual bats belonging to these groups use networks of roost trees. Forest disturbances may alter roost networks and characteristics of roost trees. Therefore, at the Fernow Experimental Forest in West Virginia, we examined roost tree networks of northern myotis (Myotis septentrionalis) in forest stands subjected to prescribed fire and in unmanipulated control treatments in 2008 and 2009. Northern myotis formed social groups whose roost areas and roost tree networks overlapped to some extent. Roost tree networks largely resembled scale-free network models, as 61% had a single central node roost tree. In control treatments, central node roost trees were in early stages of decay and surrounded by greater basal area than other trees within the networks. In prescribed fire treatments, central node roost trees were small in diameter, low in the forest canopy, and surrounded by low basal area compared to other trees in networks. Our results indicate that forest disturbances, including prescribed fire, can affect availability and distribution of roosts within roost tree networks. ?? 2011 Elsevier B.V.
Effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons
Energy Technology Data Exchange (ETDEWEB)
Li, Chun-Hsien, E-mail: chli@nknucc.nknu.edu.tw [Department of Mathematics, National Kaohsiung Normal University, Yanchao District, Kaohsiung City 82444, Taiwan (China); Yang, Suh-Yuh, E-mail: syyang@math.ncu.edu.tw [Department of Mathematics, National Central University, Jhongli District, Taoyuan City 32001, Taiwan (China)
2015-10-23
This work is devoted to investigate the effects of network structure on the synchronizability of nonlinearly coupled dynamical network of Hindmarsh–Rose neurons with a sigmoidal coupling function. We mainly focus on the networks that exhibit the small-world character or scale-free property. By checking the first nonzero eigenvalue of the outer-coupling matrix, which is closely related to the synchronization threshold, the synchronizabilities of three specific network ensembles with prescribed network structures are compared. Interestingly, we find that networks with more connections will not necessarily result in better synchronizability. - Highlights: • We investigate the effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons. • We mainly consider the networks that exhibit the small-world character or scale-free property. • The synchronizability of three specific network ensembles with prescribed network structures are compared. • Networks with more connections will not necessarily result in better synchronizability.
Research on Evolutionary Mechanism of Agile Supply Chain Network via Complex Network Theory
Directory of Open Access Journals (Sweden)
Nai-Ru Xu
2016-01-01
Full Text Available The paper establishes the evolutionary mechanism model of agile supply chain network by means of complex network theory which can be used to describe the growth process of the agile supply chain network and analyze the complexity of the agile supply chain network. After introducing the process and the suitability of taking complex network theory into supply chain network research, the paper applies complex network theory into the agile supply chain network research, analyzes the complexity of agile supply chain network, presents the evolutionary mechanism of agile supply chain network based on complex network theory, and uses Matlab to simulate degree distribution, average path length, clustering coefficient, and node betweenness. Simulation results show that the evolution result displays the scale-free property. It lays the foundations of further research on agile supply chain network based on complex network theory.
Robustness of networks against cascading failures
Dou, Bing-Lin; Wang, Xue-Guang; Zhang, Shi-Yong
2010-06-01
Inspired by other related works, this paper proposes a non-linear load-capacity model against cascading failures, which is more suitable for real networks. The simulation was executed on the B-A scale-free network, E-R random network, Internet AS level network, and the power grid of the western United States. The results show that the model is feasible and effective. By studying the relationship between network cost and robustness, we find that the model can defend against cascading failures better and requires a lower investment cost when higher robustness is required.
Robustness of network controllability in cascading failure
Chen, Shi-Ming; Xu, Yun-Fei; Nie, Sen
2017-04-01
It is demonstrated that controlling complex networks in practice needs more inputs than that predicted by the structural controllability framework. Besides, considering the networks usually faces to the external or internal failure, we define parameters to evaluate the control cost and the variation of controllability after cascades, exploring the effect of number of control inputs on the controllability for random networks and scale-free networks in the process of cascading failure. For different topological networks, the results show that the robustness of controllability will be stronger through allocating different control inputs and edge capacity.
Chambers, Joseph
2010-01-01
The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific
Multiplex congruence network of natural numbers
Yan, Xiao-Yong; Wang, Wen-Xu; Chen, Guan-Rong; Shi, Ding-Hua
2016-03-01
Congruence theory has many applications in physical, social, biological and technological systems. Congruence arithmetic has been a fundamental tool for data security and computer algebra. However, much less attention was devoted to the topological features of congruence relations among natural numbers. Here, we explore the congruence relations in the setting of a multiplex network and unveil some unique and outstanding properties of the multiplex congruence network. Analytical results show that every layer therein is a sparse and heterogeneous subnetwork with a scale-free topology. Counterintuitively, every layer has an extremely strong controllability in spite of its scale-free structure that is usually difficult to control. Another amazing feature is that the controllability is robust against targeted attacks to critical nodes but vulnerable to random failures, which also differs from ordinary scale-free networks. The multi-chain structure with a small number of chain roots arising from each layer accounts for the strong controllability and the abnormal feature. The multiplex congruence network offers a graphical solution to the simultaneous congruences problem, which may have implication in cryptography based on simultaneous congruences. Our work also gains insight into the design of networks integrating advantages of both heterogeneous and homogeneous networks without inheriting their limitations.
Transmission of HIV in sexual networks in sub-Saharan Africa and Europe
van de Vijver, David A. M. C.; Prosperi, Mattia C. F.; Ramasco, José J.
2013-09-01
We are reviewing the literature regarding sexual networks and HIV transmission in sub-Saharan Africa and Europe. On Likoma Island in Malawi, a sexual network was reconstructed using a sociometric survey in which individuals named their sexual partners. The sexual network identified one giant component including half of all sexually active individuals. More than 25% of respondents were linked through independent chains of sexual relations. HIV was more common in the sparser regions of the network due to over-representation of groups with higher HIV prevalence. A study from KwaZulu-Natal in South-Africa collected egocentric data about sexual partners and found that new infections in women in a particular area was associated with the number of life-time partners in men. Data about sexual networks and HIV transmission are not reported in Europe. It is, however, found that the annual number of sexual partners follows a scale-free network. Phylogenetic studies that determine genetic relatedness between HIV isolates obtained from infected individuals, found that patients in the early stages of infections explain a high number of new infections. In conclusion, the limited information that is available suggest that sexual networks play a role in spread of HIV. Obtaining more information about sexual networks can be of benefit for modeling studies on HIV transmission and prevention.
Controlling congestion on complex networks: fairness, efficiency and network structure.
Buzna, Ľuboš; Carvalho, Rui
2017-08-22
We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.
Emergent complex network geometry.
Wu, Zhihao; Menichetti, Giulia; Rahmede, Christoph; Bianconi, Ginestra
2015-05-18
Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we show that a single two parameter network model, the growing geometrical network, can generate complex network geometries with non-trivial distribution of curvatures, combining exponential growth and small-world properties with finite spectral dimensionality. In one limit, the non-equilibrium dynamical rules of these networks can generate scale-free networks with clustering and communities, in another limit planar random geometries with non-trivial modularity. Finally we find that these properties of the geometrical growing networks are present in a large set of real networks describing biological, social and technological systems.
Carré, Clément; Mas, André; Krouk, Gabriel
2017-01-01
Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10(4) genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data (Escherichia coli K14 network
Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model
Energy Technology Data Exchange (ETDEWEB)
Chassin, David P.; Posse, Christian
2005-09-15
The reliability of electric transmission systems is examined using a scale-free model of network topology and failure propagation. The topologies of the North American eastern and western electric grids are analyzed to estimate their reliability based on the Barabasi-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using standard power engineering methods, and they suggest that scale-free network models are usable to estimate aggregate electric grid reliability.
Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model
Energy Technology Data Exchange (ETDEWEB)
Chassin, David P.; Posse, Christian
2005-09-15
The reliability of electric transmission systems is examined using a scale-free model of network topology and failure propagation. The topologies of the North American eastern and western electric grids are analyzed to estimate their reliability based on the Barabási-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using other methods and they suggest that scale-free network models are usable to estimate aggregate electric grid reliability.
Public authority control strategy for opinion evolution in social networks
Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei
2016-08-01
This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.
Quantum Google algorithm. Construction and application to complex networks
Paparo, G. D.; Müller, M.; Comellas, F.; Martin-Delgado, M. A.
2014-07-01
We review the main findings on the ranking capabilities of the recently proposed Quantum PageRank algorithm (G.D. Paparo et al., Sci. Rep. 2, 444 (2012) and G.D. Paparo et al., Sci. Rep. 3, 2773 (2013)) applied to large complex networks. The algorithm has been shown to identify unambiguously the underlying topology of the network and to be capable of clearly highlighting the structure of secondary hubs of networks. Furthermore, it can resolve the degeneracy in importance of the low-lying part of the list of rankings. Examples of applications include real-world instances from the WWW, which typically display a scale-free network structure and models of hierarchical networks. The quantum algorithm has been shown to display an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance among the nodes, as compared to the classical algorithm.
Public authority control strategy for opinion evolution in social networks.
Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei
2016-08-01
This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.
Cavallaro, Kathleen F; Sandhu, Hardeep S; Hyde, Terri B; Johnson, Barbara W; Fischer, Marc; Mayer, Leonard W; Clark, Thomas A; Pallansch, Mark A; Yin, Zundong; Zuo, Shuyan; Hadler, Stephen C; Diorditsa, Serguey; Hasan, A S M Mainul; Bose, Anindya S; Dietz, Vance
2015-02-25
Surveillance for acute flaccid paralysis with laboratory confirmation has been a key strategy in the global polio eradication initiative, and the laboratory platform established for polio testing has been expanded in many countries to include surveillance for cases of febrile rash illness to identify measles and rubella cases. Vaccine-preventable disease surveillance is essential to detect outbreaks, define disease burden, guide vaccination strategies and assess immunization impact. Vaccines now exist to prevent Japanese encephalitis (JE) and some etiologies of bacterial meningitis. We evaluated the feasibility of expanding polio-measles surveillance and laboratory networks to detect bacterial meningitis and JE, using surveillance for acute meningitis-encephalitis syndrome in Bangladesh and China and acute encephalitis syndrome in India. We developed nine syndromic surveillance performance indicators based on international surveillance guidelines and calculated scores using supervisory visit reports, annual reports, and case-based surveillance data. Scores, variable by country and targeted disease, were highest for the presence of national guidelines, sustainability, training, availability of JE laboratory resources, and effectiveness of using polio-measles networks for JE surveillance. Scores for effectiveness of building on polio-measles networks for bacterial meningitis surveillance and specimen referral were the lowest, because of differences in specimens and techniques. Polio-measles surveillance and laboratory networks provided useful infrastructure for establishing syndromic surveillance and building capacity for JE diagnosis, but were less applicable for bacterial meningitis. Laboratory-supported surveillance for vaccine-preventable bacterial diseases will require substantial technical and financial support to enhance local diagnostic capacity. Published by Elsevier Ltd.
Complex Network Simulation of Forest Network Spatial Pattern in Pearl River Delta
Zeng, Y.
2017-09-01
Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network's power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network's degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network's main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.
Directory of Open Access Journals (Sweden)
Angel Garrido
2011-01-01
Full Text Available In this paper, we analyze a few interrelated concepts about graphs, such as their degree, entropy, or their symmetry/asymmetry levels. These concepts prove useful in the study of different types of Systems, and particularly, in the analysis of Complex Networks. A System can be defined as any set of components functioning together as a whole. A systemic point of view allows us to isolate a part of the world, and so, we can focus on those aspects that interact more closely than others. Network Science analyzes the interconnections among diverse networks from different domains: physics, engineering, biology, semantics, and so on. Current developments in the quantitative analysis of Complex Networks, based on graph theory, have been rapidly translated to studies of brain network organization. The brain's systems have complex network features—such as the small-world topology, highly connected hubs and modularity. These networks are not random. The topology of many different networks shows striking similarities, such as the scale-free structure, with the degree distribution following a Power Law. How can very different systems have the same underlying topological features? Modeling and characterizing these networks, looking for their governing laws, are the current lines of research. So, we will dedicate this Special Issue paper to show measures of symmetry in Complex Networks, and highlight their close relation with measures of information and entropy.
Travel and tourism: Into a complex network
Miguéns, J. I. L.; Mendes, J. F. F.
2008-05-01
It is discussed how the worldwide tourist arrivals, about 10% of the world’s domestic product, form a largely heterogeneous and directed complex network. Remarkably the random network of connectivity is converted into a scale-free network of intensities. The importance of weights on network connections is brought into discussion. It is also shown how strategic positioning particularly benefits from market diversity and that interactions among countries prevail on a technological and economic pattern, questioning the backbone of driving forces in traveling.
Froli, Maurizio; Laccone, Francesco
2017-09-01
Grid shells supporting transparent or opaque panels are largely used to cover long-spanned spaces because of their lightness, the easy setup, and economy. This paper presents the results of experimental static and dynamic investigations carried out on a large-scale free-form grid shell mock-up, whose geometry descended from an innovative Voronoi polygonal pattern. Accompanying finite-element method (FEM) simulations followed. To these purposes, a four-step procedure was adopted: (1) a perfect FEM model was analyzed; (2) using the modal shapes scaled by measuring the mock-up, a deformed unloaded geometry was built, which took into account the defects caused by the assembly phase; (3) experimental static tests were executed by affixing weights to the mock-up, and a simplified representative FEM model was calibrated, choosing the nodes stiffness and the material properties as parameters; and (4) modal identification was performed through operational modal analysis and impulsive tests, and then, a simplified FEM dynamical model was calibrated. Due to the high deformability of the mock-up, only a symmetric load case configuration was adopted.
Mediated attachment as a mechanism for growth of complex networks
Shekatkar, Snehal M
2014-01-01
Connection topologies of many networked systems like human brain, biological cell, world wide web, power grids, human society and ecological food webs markedly deviate from that of completely random networks indicating the presence of organizing principles behind their evolution. The five important features that characterize such networks are scale-free topology, small average path length, high clustering, hierarchical community structure and assortative mixing. Till now the generic mechanisms underlying the existence of these properties are not well understood. Here we show that potentially a single mechanism, which we call "mediated attachment", where two nodes get connected through a mediator or common neighbor, could be responsible for the emergence of all important properties of real networks. The mediated attachment naturally unifies scale-free topology, high clustering, small world nature, hierarchical community structure and dissortative nature of networks. Further, with additional mixing by age, this...
Epidemics scenarios in the "Romantic network"
Carvalho, Alexsandro M
2012-01-01
The structure of sexual contacts, its contacts network and its temporal interactions, play an important role in the spread of sexually transmitted infections. Unfortunately, that kind of data is very hard to obtain. One of the few exceptions is the "Romantic network" which is a complete structure of a real sexual network of a high school. In terms of topology, unlike other sexual networks classified as scale-free network. Regarding the temporal structure, several studies indicate that relationship timing can have effects on diffusion through networks, as relationship order determines transmission routes.With the aim to check if the particular structure, static and dynamic, of the Romantic network is determinant for the propagation of an STI in it, we perform simulations in two scenarios: the static network where all contacts are available and the dynamic case where contacts evolve in time. In the static case, we compare the epidemic results in the Romantic network with some paradigmatic topologies. We further...
PageRank model of opinion formation on Ulam networks
Chakhmakhchyan, L.; Shepelyansky, D.
2013-12-01
We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks has certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.
Co-occurrence network analysis of modern Chinese poems
Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong
2015-02-01
A total of 606 co-occurrence networks of Chinese characters and words are constructed from rhymes, free verses, and prose poems. It is found that 98.5 % of networks have scale-free properties, while 19.8 % of networks do not have small-world features, especially the clustering coefficients in 5.6 % of networks are zero. In addition, 61.4 % of networks have significant hierarchical structures, and 98 % of networks are disassortative. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.
Complex Network for Solar Active Regions
Daei, Farhad; Safari, Hossein; Dadashi, Neda
2017-08-01
In this paper we developed a complex network of solar active regions (ARs) to study various local and global properties of the network. The values of the Hurst exponent (0.8-0.9) were evaluated by both the detrended fluctuation analysis and the rescaled range analysis applied on the time series of the AR numbers. The findings suggest that ARs can be considered as a system of self-organized criticality (SOC). We constructed a growing network based on locations, occurrence times, and the lifetimes of 4227 ARs recorded from 1999 January 1 to 2017 April 14. The behavior of the clustering coefficient shows that the AR network is not a random network. The logarithmic behavior of the length scale has the characteristics of a so-called small-world network. It is found that the probability distribution of the node degrees for undirected networks follows the power law with exponents of about 3.7-4.2. This indicates the scale-free nature of the AR network. The scale-free and small-world properties of the AR network confirm that the system of ARs forms a system of SOC. Our results show that the occurrence probability of flares (classified by GOES class C> 5, M, and X flares) in the position of the AR network hubs takes values greater than that obtained for other nodes.
Nunes Amaral, Luis A.
2002-03-01
We study the statistical properties of a variety of diverse real-world networks including the neural network of C. Elegans, food webs for seven distinct environments, transportation and technological networks, and a number of distinct social networks [1-5]. We present evidence of the occurrence of three classes of small-world networks [2]: (a) scale-free networks, characterized by a vertex connectivity distribution that decays as a power law; (b) broad-scale networks, characterized by a connectivity distribution that has a power-law regime followed by a sharp cut-off; (c) single-scale networks, characterized by a connectivity distribution with a fast decaying tail. Moreover, we note for the classes of broad-scale and single-scale networks that there are constraints limiting the addition of new links. Our results suggest that the nature of such constraints may be the controlling factor for the emergence of different classes of networks. [See http://polymer.bu.edu/ amaral/Networks.html for details and htpp://polymer.bu.edu/ amaral/Professional.html for access to PDF files of articles.] 1. M. Barthélémy, L. A. N. Amaral, Phys. Rev. Lett. 82, 3180-3183 (1999). 2. L. A. N. Amaral, A. Scala, M. Barthélémy, H. E. Stanley, Proc. Nat. Acad. Sci. USA 97, 11149-11152 (2000). 3. F. Liljeros, C. R. Edling, L. A. N. Amaral, H. E. Stanley, and Y. Åberg, Nature 411, 907-908 (2001). 4. J. Camacho, R. Guimera, L.A.N. Amaral, Phys. Rev. E RC (to appear). 5. S. Mossa, M. Barthelemy, H.E. Stanley, L.A.N. Amaral (submitted).
Efficient transmission of subthreshold signals in complex networks of spiking neurons.
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Joaquin J Torres
Full Text Available We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.
Efficient transmission of subthreshold signals in complex networks of spiking neurons.
Torres, Joaquin J; Elices, Irene; Marro, J
2015-01-01
We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.
Structurally Dynamic Spin Market Networks
Horváth, Denis; Kuscsik, Zoltán
The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.
COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN IN PEARL RIVER DELTA
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Y. Zeng
2017-09-01
Full Text Available Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc. for networking a standard and base datum.
Comparison of co-occurrence networks of the Chinese and English languages
Liang, Wei; Shi, Yuming; Tse, Chi K.; Liu, Jing; Wang, Yanli; Cui, Xunqiang
2009-12-01
Co-occurrence networks of Chinese characters and words, and of English words, are constructed from collections of Chinese and English articles, respectively. Four types of collections are considered, namely, essays, novels, popular science articles, and news reports. Statistical parameters of the networks are studied, including diameter, average degree, degree distribution, clustering coefficient, average shortest path length, as well as the number of connected subnetworks. It is found that the character and word networks of each type of article in the Chinese language, and the word network of each type of article in the English language all exhibit scale-free and small-world features. The statistical parameters of these co-occurrence networks are compared within the same language and across the two languages. This study reveals some commonalities and differences between Chinese and English languages, and among the four types of articles in each language from a complex network perspective. In particular, it is shown that expressions in English are briefer than those in Chinese in a certain sense.
Constraints and entropy in a model of network evolution
Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.
2017-11-01
Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.
DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.
Energy Technology Data Exchange (ETDEWEB)
MASLOV,S.SNEPPEN,K.
2003-11-17
interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which
Spectral coarse grained controllability of complex networks
Wang, Pei; Xu, Shuang
2017-07-01
With the accumulation of interaction data from various systems, a fundamental question in network science is how to reduce the sizes while keeping certain properties of complex networks. Combined the spectral coarse graining theory and the structural controllability of complex networks, we explore the structural controllability of undirected complex networks during coarse graining processes. We evidence that the spectral coarse grained controllability (SCGC) properties for the Erdös-Rényi (ER) random networks, the scale-free (SF) random networks and the small-world (SW) random networks are distinct from each other. The SW networks are very robust, while the SF networks are sensitive during the coarse graining processes. As an emergent properties for the dense ER networks, during the coarse graining processes, there exists a threshold value of the coarse grained sizes, which separates the controllability of the reduced networks into robust and sensitive to coarse graining. Investigations on some real-world complex networks indicate that the SCGC properties are varied among different categories and different kinds of networks, some highly organized social or biological networks are more difficult to be controlled, while many man-made power networks and infrastructure networks can keep the controllability properties during the coarse graining processes. Furthermore, we speculate that the SCGC properties of complex networks may depend on their degree distributions. The associated investigations have potential implications in the control of large-scale complex networks, as well as in the understanding of the organization of complex networks.
Network topology analysis approach on China's QFII stock investment behavior
Zhang, Yongjie; Cao, Xing; He, Feng; Zhang, Wei
2017-05-01
In this paper, the investment behavior of QFII in China stock market from 2004 to 2015 is studied with the network topology method. Based on the nodes topological characteristics, stock holding fluctuations correlation is studied from the micro network level. We conclude that the QFII mutual stock holding network have both scale free and small world properties, which presented mainly small world characteristics from 2005 to 2011, and scale free characteristics from 2012 to 2015. Moreover, fluctuations correlation is different with different nodes topological characteristics. In different economic periods, QFII represented different connection patterns and they reacted to the market crash spontaneously. Thus, this paper provides the first evidence of complex network research on QFII' investment behavior in China as an emerging market.
Structural Behavioral Study on the General Aviation Network Based on Complex Network
Zhang, Liang; Lu, Na
2017-12-01
The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.
Iannone, Eugenio
2011-01-01
Many argue that telecommunications network infrastructure is the most impressive and important technology ever developed. Analyzing the telecom market's constantly evolving trends, research directions, infrastructure, and vital needs, Telecommunication Networks responds with revolutionized engineering strategies to optimize network construction. Omnipresent in society, telecom networks integrate a wide range of technologies. These include quantum field theory for the study of optical amplifiers, software architectures for network control, abstract algebra required to design error correction co
Topology of the conceptual network of language
Motter, Adilson E.; de Moura, Alessandro P.; Lai, Ying-Cheng; Dasgupta, Partha
2002-06-01
We define two words in a language to be connected if they express similar concepts. The network of connections among the many thousands of words that make up a language is important not only for the study of the structure and evolution of languages, but also for cognitive science. We study this issue quantitatively, by mapping out the conceptual network of the English language, with the connections being defined by the entries in a Thesaurus dictionary. We find that this network presents a small-world structure, with an amazingly small average shortest path, and appears to exhibit an asymptotic scale-free feature with algebraic connectivity distribution.
Discovering large network motifs from a complex biological network
Terada, Aika; Sese, Jun
2009-12-01
Graph structures representing relationships between entries have been studied in statistical analysis, and the results of these studies have been applied to biological networks, whose nodes and edges represent proteins and the relationships between them, respectively. Most of the studies have focused on only graph structures such as scale-free properties and cliques, but the relationships between nodes are also important features since most of the proteins perform their functions by connecting to other proteins. In order to determine such relationships, the problem of network motif discovery has been addressed; network motifs are frequently appearing graph structures in a given graph. However, the methods for network motif discovery are highly restrictive for the application to biological network because they can only be used to find small network motifs or they do not consider noise and uncertainty in observations. In this study, we introduce a new index to measure network motifs called AR index and develop a novel algorithm called ARIANA for finding large motifs even when the network has noise. Experiments using a synthetic network verify that our method can find better network motifs than an existing algorithm. By applying ARIANA to a real complex biological network, we find network motifs associated with regulations of start time of cell functions and generation of cell energies and discover that the cell cycle proteins can be categorized into two different groups.
Distributed flow optimization and cascading effects in weighted complex networks
Asztalos, Andrea; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, G.
2011-01-01
We investigate the effect of a specific edge weighting scheme $\\sim (k_i k_j)^{\\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficie...
Gossip spread in social network Models
Johansson, Tobias
2017-04-01
Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.
Simulated Associating Polymer Networks
Billen, Joris
Telechelic associating polymer networks consist of polymer chains terminated by endgroups that have a different chemical composition than the polymer backbone. When dissolved in a solution, the endgroups cluster together to form aggregates. At low temperature, a strongly connected reversible network is formed and the system behaves like a gel. Telechelic networks are of interest since they are representative for biopolymer networks (e.g. F-actin) and are widely used in medical applications (e.g. hydrogels for tissue engineering, wound dressings) and consumer products (e.g. contact lenses, paint thickeners). In this thesis such systems are studied by means of a molecular dynamics/Monte Carlo simulation. At first, the system in rest is studied by means of graph theory. The changes in network topology upon cooling to the gel state, are characterized. Hereto an extensive study of the eigenvalue spectrum of the gel network is performed. As a result, an in-depth investigation of the eigenvalue spectra for spatial ER, scale-free, and small-world networks is carried out. Next, the gel under the application of a constant shear is studied, with a focus on shear banding and the changes in topology under shear. Finally, the relation between the gel transition and percolation is discussed.
Functional Aspects of Biological Networks
Sneppen, Kim
2007-03-01
We discuss biological networks with respect to 1) relative positioning and importance of high degree nodes, 2) function and signaling, 3) logic and dynamics of regulation. Visually the soft modularity of many real world networks can be characterized in terms of number of high and low degrees nodes positioned relative to each other in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). In these terms biological networks looks like rugged landscapes with separated peaks, hub proteins, which each are roughly as essential as any of the individual proteins on the periphery of the hub. Within each sup-domain of a molecular network one can often identify dynamical feedback mechanisms that falls into combinations of positive and negative feedback circuits. We will illustrate this with examples taken from phage regulation and bacterial uptake and regulation of small molecules. In particular we find that a double negative regulation often are replaced by a single positive link in unrelated organisms with same functional requirements. Overall we argue that network topology primarily reflects functional constraints. References: S. Maslov and K. Sneppen. ``Computational architecture of the yeast regulatory network." Phys. Biol. 2:94 (2005) A. Trusina et al. ``Functional alignment of regulatory networks: A study of temerate phages". Plos Computational Biology 1:7 (2005). J.B. Axelsen et al. ``Degree Landscapes in Scale-Free Networks" physics/0512075 (2005). A. Trusina et al. ``Hierarchy and Anti-Hierarchy in Real and Scale Free networks." PRL 92:178702 (2004) S. Semsey et al. ``Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli". (2006) q-bio.MN/0609042
34 CFR 303.15 - Include; including.
2010-07-01
... 34 Education 2 2010-07-01 2010-07-01 false Include; including. 303.15 Section 303.15 Education Regulations of the Offices of the Department of Education (Continued) OFFICE OF SPECIAL EDUCATION AND REHABILITATIVE SERVICES, DEPARTMENT OF EDUCATION EARLY INTERVENTION PROGRAM FOR INFANTS AND TODDLERS WITH...
Scaling in public transport networks
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C. von Ferber
2005-01-01
Full Text Available We analyse the statistical properties of public transport networks. These networks are defined by a set of public transport routes (bus lines and the stations serviced by these. For larger networks these appear to possess a scale-free structure, as it is demonstrated e.g. by the Zipf law distribution of the number of routes servicing a given station or for the distribution of the number of stations which can be visited from a chosen one without changing the means of transport. Moreover, a rather particular feature of the public transport network is that many routes service common subsets of stations. We discuss the possibility of new scaling laws that govern intrinsic properties of such subsets.
Evolution of opinions on social networks in the presence of competing committed groups.
Xie, Jierui; Emenheiser, Jeffrey; Kirby, Matthew; Sreenivasan, Sameet; Szymanski, Boleslaw K; Korniss, Gyorgy
2012-01-01
Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about 10% of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions A and B, and constituting fractions pA and pB of the total population respectively, are present in the network. We show for stylized social networks (including Erdös-Rényi random graphs and Barabási-Albert scale-free networks) that the phase diagram of this system in parameter space (pA,pB) consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point.
Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher
2005-01-01
This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.
Controllability of the better chosen partial networks
Liu, Xueming; Pan, Linqiang
2016-08-01
How to control large complex networks is a great challenge. Recent studies have proved that the whole network can be sufficiently steered by injecting control signals into a minimum set of driver nodes, and the minimum numbers of driver nodes for many real networks are high, indicating that it is difficult to control them. For some large natural and technological networks, it is impossible and not feasible to control the full network. For example, in biological networks like large-scale gene regulatory networks it is impossible to control all the genes. This prompts us to explore the question how to choose partial networks that are easy for controlling and important in networked systems. In this work, we propose a method to achieve this goal. By computing the minimum driver nodes densities of the partial networks of Erdös-Rényi (ER) networks, scale-free (SF) networks and 23 real networks, we find that our method performs better than random method that chooses nodes randomly. Moreover, we find that the nodes chosen by our method tend to be the essential elements of the whole systems, via studying the nodes chosen by our method of a real human signaling network and a human protein interaction network and discovering that the chosen nodes from these networks tend to be cancer-associated genes. The implementation of our method shows some interesting connections between the structure and the controllability of networks, improving our understanding of the control principles of complex systems.
Evolution of weighted complex bus transit networks with flow
Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei
2016-02-01
Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.
Fast network centrality analysis using GPUs
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Shi Zhiao
2011-05-01
Full Text Available Abstract Background With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs. Results We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package gpu-fan (GPU-based Fast Analysis of Networks for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks. Conclusions gpu-fan provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL at http://bioinfo.vanderbilt.edu/gpu-fan/.
Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.
Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E
2018-02-07
This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Wang, Jinlian; Zuo, Yiming; Liu, Lun; Man, Yangao; Tadesse, Mahlet G; Ressom, Habtom W
2014-04-01
Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein-protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.
Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.
Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang
2012-01-01
Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.
Co-occurrence network analysis of Chinese and English poems
Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong
2015-02-01
A total of 572 co-occurrence networks of Chinese characters and words as well as English words are constructed from both Chinese and English poems. It is found that most of the networks have small-world features; more Chinese networks have scale-free properties and hierarchical structures as compared with the English networks; all the networks are disassortative, and the disassortativeness of the Chinese word networks is more prominent than those of the English networks; the spectral densities of the Chinese word networks and English networks are similar, but they are different from those of the ER, BA, and WS networks. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.
2016-01-01
This volume provides an introduction to and overview of the emerging field of interconnected networks which include multi layer or multiplex networks, as well as networks of networks. Such networks present structural and dynamical features quite different from those observed in isolated networks. The presence of links between different networks or layers of a network typically alters the way such interconnected networks behave – understanding the role of interconnecting links is therefore a crucial step towards a more accurate description of real-world systems. While examples of such dissimilar properties are becoming more abundant – for example regarding diffusion, robustness and competition – the root of such differences remains to be elucidated. Each chapter in this topical collection is self-contained and can be read on its own, thus making it also suitable as reference for experienced researchers wishing to focus on a particular topic.
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
Epidemic spreading on networks based on stress response
Nian, Fuzhong; Yao, Shuanglong
2017-06-01
Based on the stress responses of individuals, the susceptible-infected-susceptible epidemic model was improved on the small-world networks and BA scale-free networks and the simulations were implemented and analyzed. Results indicate that the behaviors of individual’s stress responses could induce the epidemic spreading resistance and adaptation at the network level. This phenomenon showed that networks were learning how to adapt to the disease and the evolution process could improve their immunization to future infectious diseases and would effectively prevent the spreading of infectious diseases.
Multiplex Networks of the Guarantee Market: Evidence from China
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Shouwei Li
2017-01-01
Full Text Available We investigate a multiplex network of the guarantee market with three layers corresponding to different types of guarantee relationships in China. We find that three single-layer networks all have the scale-free property and are of disassortative nature. A single-layer network is not quite representative of another single-layer network. The result of the betweenness centrality shows that central companies in one layer are not necessarily central in another layer. And the eigenvector centrality has the same result of the betweenness centrality except the top central company.
Barabasi, Albert-Laszlo
2016-01-01
Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network sci...
An adaptive complex network model for brain functional networks.
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Ignacio J Gomez Portillo
Full Text Available Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.
DARPA Ensemble-Based Modeling Large Graphs & Applications to Social Networks
2015-07-29
Korniss Other Highlights o N.V. Chawla received a 2012 IBM Watson Faculty Award and a 2013 IBM Big Data and Analytics Award. o Z. Toroczkai...SC, Apr 30, 2014 25. F. Molnár, “Minimum Dominating Sets in Scale-Free Networks" at the IBM T. J. Watson Research, Yorktown Heights, NY (April 16
Spreading gossip in social networks
Lind, Pedro G.; da Silva, Luciano R.; Andrade, José S., Jr.; Herrmann, Hans J.
2007-09-01
We study a simple model of information propagation in social networks, where two quantities are introduced: the spread factor, which measures the average maximal reachability of the neighbors of a given node that interchange information among each other, and the spreading time needed for the information to reach such a fraction of nodes. When the information refers to a particular node at which both quantities are measured, the model can be taken as a model for gossip propagation. In this context, we apply the model to real empirical networks of social acquaintances and compare the underlying spreading dynamics with different types of scale-free and small-world networks. We find that the number of friendship connections strongly influences the probability of being gossiped. Finally, we discuss how the spread factor is able to be applied to other situations.
Network Frontier Workshop 2013
2014-11-11
networks, biological networks, cognitive and semantic networks and social networks. This field has received a major boost caused by the availability of huge...networks, which require new ways of thinking about the world. Part of the new cognition is provided by the fractional calculus description of temporal...structures in a wide range of examples—including road networks in large urban areas, a rabbit warren, a dolphin social network, a European interbank network
Bassett, Danielle S; Sporns, Olaf
2017-02-23
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
The Laplacian spectrum of neural networks
de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.
2014-01-01
The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286
Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures.
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Xingyuan Wang
Full Text Available Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks is more robust than ER-ER (two coupled random networks or SF-ER (coupled random network and scale-free network. This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids.
Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures
Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng
2016-01-01
Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. PMID:27494715
Comparison of directed and weighted co-occurrence networks of six languages
Gao, Yuyang; Liang, Wei; Shi, Yuming; Huang, Qiuling
2014-01-01
To study commonalities and differences among different languages, we select 100 reports from the documents of the United Nations, each of which was written in Arabic, Chinese, English, French, Russian and Spanish languages, separately. Based on these corpora, we construct 6 weighted and directed word co-occurrence networks. Besides all the networks exhibit scale-free and small-world features, we find several new non-trivial results, including connections among English words are denser, and the expression of English language is more flexible and powerful; the connection way among Spanish words is more stringent and this indicates that the Spanish grammar is more rigorous; values of many statistical parameters of the French and Spanish networks are very approximate and this shows that these two languages share many commonalities; Arabic and Russian words have many varieties, which result in rich types of words and a sparse connection among words; connections among Chinese words obey a more uniform distribution, and one inclines to use the least number of Chinese words to express the same complex information as those in other five languages. This shows that the expression of Chinese language is quite concise. In addition, several topics worth further investigating by the complex network approach have been observed in this study.
Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory
Wang, Na; Li, Dong; Wang, Qiwen
2012-12-01
The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government
Spatial Scaling of Land Cover Networks
Small, Christopher
2015-01-01
Spatial networks of land cover are well-described by power law rank-size distributions. Continuous field proxies for human settlements, agriculture and forest cover have similar spatial scaling properties spanning 4 to 5 orders of magnitude. Progressive segmentation of these continuous fields yields spatial networks with rank-size distributions having slopes near -1 for a wide range of thresholds. We consider a general explanation for this scaling that does not require different processes for each type of land cover. The same conditions that give rise to scale-free networks in general can produce power law distributions of component sizes for bounded spatial networks confined to a plane or surface. Progressive segmentation of a continuous field naturally results in growth of the network while the increasing perimeters of the growing components result in preferential attachment to the larger components with the longer perimeters. Progressive segmentation of two types of random continuous field results in progr...
Borge-Holthoefer, Javier; Rivero, Alejandro; García, Iñigo; Cauhé, Elisa; Ferrer, Alfredo; Ferrer, Darío; Francos, David; Iñiguez, David; Pérez, María Pilar; Ruiz, Gonzalo; Sanz, Francisco; Serrano, Fermín; Viñas, Cristina; Tarancón, Alfonso; Moreno, Yamir
2011-01-01
The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.
Directory of Open Access Journals (Sweden)
Javier Borge-Holthoefer
Full Text Available The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.
Information Horizons in Complex Networks
Sneppen, Kim
2005-03-01
We investigate how the structure constrain specific communication in social-, man-made and biological networks. We find that human networks of governance and collaboration are predictable on teat-a-teat level, reflecting well defined pathways, but globally inefficient (1). In contrast, the Internet tends to have better overall communication abilities, more alternative pathways, and is therefore more robust. Between these extremes are the molecular network of living organisms. Further, for most real world networks we find that communication ability is favored by topology on small distances, but disfavored at larger distances (2,3,4). We discuss the topological implications in terms of modularity and the positioning of hubs in the networks (5,6). Finally we introduce some simple models which demonstarte how communication may shape the structure of in particular man made networks (7,8). 1) K. Sneppen, A. Trusina, M. Rosvall (2004). Hide and seek on complex networks [cond-mat/0407055] 2) M. Rosvall, A. Trusina, P. Minnhagen and K. Sneppen (2004). Networks and Cities: An Information Perspective [cond-mat/0407054]. In PRL. 3) A. Trusina, M. Rosvall, K. Sneppen (2004). Information Horizons in Networks. [cond-mat/0412064] 4) M. Rosvall, P. Minnhagen, K. Sneppen (2004). Navigating Networks with Limited Information. [cond-mat/0412051] 5) S. Maslov and K. Sneppen (2002). Specificity and stability in topology of protein networks Science 296, 910-913 [cond-mat/0205380]. 6) A. Trusina, S. Maslov, P. Minnhagen, K. Sneppen Hierarchy Measures in Complex Networks. Phys. Rev. Lett. 92, 178702 [cond-mat/0308339]. 7) M. Rosvall and K. Sneppen (2003). Modeling Dynamics of Information Networks. Phys. Rev. Lett. 91, 178701 [cond-mat/0308399]. 8) B-J. Kim, A. Trusina, P. Minnhagen, K. Sneppen (2003). Self Organized Scale-Free Networks from Merging and Regeneration. nlin.AO/0403006. In European Journal of Physics.
Ranking stability and super-stable nodes in complex networks.
Ghoshal, Gourab; Barabási, Albert-László
2011-07-19
Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.
Heterogeneous Epidemic Model for Assessing Data Dissemination in Opportunistic Networks
DEFF Research Database (Denmark)
Rozanova, Liudmila; Alekseev, Vadim; Temerev, Alexander
2014-01-01
that amount of data transferred between network nodes possesses a Pareto distribution, implying scale-free properties. In this context, more heterogeneity in susceptibility means the less severe epidemic progression, and, on the contrary, more heterogeneity in infectivity leads to more severe epidemics...... — assuming that the other parameter (either heterogeneity or susceptibility) stays fixed. The results are general enough to be useful for estimating the epidemic progression with no significant acquired immunity — in the cases where Pareto distribution holds....
A Complex Network Approach to Distributional Semantic Models.
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Akira Utsumi
Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.
Optical modulator including grapene
Energy Technology Data Exchange (ETDEWEB)
Liu, Ming; Yin, Xiaobo; Zhang, Xiang
2016-06-07
The present invention provides for a one or more layer graphene optical modulator. In a first exemplary embodiment the optical modulator includes an optical waveguide, a nanoscale oxide spacer adjacent to a working region of the waveguide, and a monolayer graphene sheet adjacent to the spacer. In a second exemplary embodiment, the optical modulator includes at least one pair of active media, where the pair includes an oxide spacer, a first monolayer graphene sheet adjacent to a first side of the spacer, and a second monolayer graphene sheet adjacent to a second side of the spacer, and at least one optical waveguide adjacent to the pair.
Nobelist TD LEE Scientist Cooperation Network and Scientist Innovation Ability Model
Fang, Jin-Qing; Liu, Qiang
2013-01-01
Nobelist TD Lee scientist cooperation network (TDLSCN) and their innovation ability are studied. It is found that the TDLSCN not only has the common topological properties both of scale-free and small-world for a general scientist cooperation networks, but also appears the creation multiple-peak phenomenon for number of published paper with year evolution, which become Nobelist TD Lee’s significant mark distinguished from other scientists. This new phenomenon has not been revealed in the scie...
Dynamic analysis of a sexually transmitted disease model on complex networks
Yuan, Xin-Peng; Xue, Ya-Kui; Liu, Mao-Xing
2013-03-01
In this paper, a sexually transmitted disease model is proposed on complex networks, where contacts between humans are treated as a scale-free social network. There are three groups in our model, which are dangerous male, non-dangerous male, and female. By mathematical analysis, we obtain the basic reproduction number for the existence of endemic equilibrium and study the effects of various immunization schemes about different groups. Furthermore, numerical simulations are undertaken to verify more conclusions.
Combined Heuristic Attack Strategy on Complex Networks
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Marek Šimon
2017-01-01
Full Text Available Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.
Growing Homophilic Networks Are Natural Navigable Small Worlds.
Malkov, Yury A; Ponomarenko, Alexander
2016-01-01
Navigability, an ability to find a logarithmically short path between elements using only local information, is one of the most fascinating properties of real-life networks. However, the exact mechanism responsible for the formation of navigation properties remained unknown. We show that navigability can be achieved by using only two ingredients present in the majority of networks: network growth and local homophily, giving a persuasive answer how the navigation appears in real-life networks. A very simple algorithm produces hierarchical self-similar optimally wired navigable small world networks with exponential degree distribution by using only local information. Adding preferential attachment produces a scale-free network which has shorter greedy paths, but worse (power law) scaling of the information extraction locality (algorithmic complexity of a search). Introducing saturation of the preferential attachment leads to truncated scale-free degree distribution that offers a good tradeoff between these parameters and can be useful for practical applications. Several features of the model are observed in real-life networks, in particular in the brain neural networks, supporting the earlier suggestions that they are navigable.
Growing Homophilic Networks Are Natural Navigable Small Worlds.
Directory of Open Access Journals (Sweden)
Yury A Malkov
Full Text Available Navigability, an ability to find a logarithmically short path between elements using only local information, is one of the most fascinating properties of real-life networks. However, the exact mechanism responsible for the formation of navigation properties remained unknown. We show that navigability can be achieved by using only two ingredients present in the majority of networks: network growth and local homophily, giving a persuasive answer how the navigation appears in real-life networks. A very simple algorithm produces hierarchical self-similar optimally wired navigable small world networks with exponential degree distribution by using only local information. Adding preferential attachment produces a scale-free network which has shorter greedy paths, but worse (power law scaling of the information extraction locality (algorithmic complexity of a search. Introducing saturation of the preferential attachment leads to truncated scale-free degree distribution that offers a good tradeoff between these parameters and can be useful for practical applications. Several features of the model are observed in real-life networks, in particular in the brain neural networks, supporting the earlier suggestions that they are navigable.
Visual Impairment, Including Blindness
... Who Knows What? (log-in required) Select Page Visual Impairment, Including Blindness Mar 31, 2017 Links updated, ... doesn’t wear his glasses. Back to top Visual Impairments in Children Vision is one of our ...
Csermely, Peter
2008-01-01
Active centres and hot spots of proteins have a paramount importance in enzyme action, protein complex formation and drug design. Recently a number of publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show a number of properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks from molecules to the society. Based on extensive analogies I propose that the existing findings and methodology enable us to detect active centres in cells, social networks and ecosystems. Members of these active centres are creative elements of the respective networks, which may help them to survive unprecedented, novel challenges, and play a key role in the development, survival and evolvability of complex systems.
A last updating evolution model for online social networks
Bu, Zhan; Xia, Zhengyou; Wang, Jiandong; Zhang, Chengcui
2013-05-01
As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.
Veck, Wayne
2009-01-01
This paper attempts to make important connections between listening and inclusive education and the refusal to listen and exclusion. Two lines of argument are advanced. First, if educators and learners are to include each other within their educational institutions as unique individuals, then they will need to listen attentively to each other.…
Jodi Dean
2010-01-01
This article sets out the idea of affective networks as a constitutive feature of communicative capitalism. It explores the circulation of intensities in contemporary information and communication networks, arguing that this circulation should be theorized in terms of the psychoanalytic notion of the drive. The article includes critical engagements with theorists such as Guy Debord, Jacques Lacan, Tiziana Terranova, and Slavoj Zizek.
Robustness and Optimization of Complex Networks : Reconstructability, Algorithms and Modeling
Liu, D.
2013-01-01
The infrastructure networks, including the Internet, telecommunication networks, electrical power grids, transportation networks (road, railway, waterway, and airway networks), gas networks and water networks, are becoming more and more complex. The complex infrastructure networks are crucial to our
Interdependent networks - Topological percolation research and application in finance
Zhou, Di
This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks and applying simulation and numerical methods to study the robustness of the network system, and ii) applying statistical physics concepts and methods to quantitatively analyze complex systems and applying the theoretical framework to study real-world systems. In part I, we focus on developing theories of interdependent networks as well as building computer simulation models, which includes three parts: 1) We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the networks significantly changes the critical density of failures, which can trigger the total disruption of the two-network system. Specifically, we find that the assortativity within a single network decreases the robustness of the entire system. 2) We study the percolation behavior of two interdependent scale-free (SF) networks under random failure of 1-p fraction of nodes. We find that as the coupling strength q between the two networks reduces from 1 (fully coupled) to 0 (no coupling), there exist two critical coupling strengths q1 and q2 , which separate the behaviors of the giant component as a function of p into three different regions, and for q2 numerically. We study a starlike network of n Erdos-Renyi (ER), SF networks and a looplike network of n ER networks, and we find for starlike networks, their phase transition regions change with n, but for looplike networks the phase regions change with average degree k . In part II, we apply concepts and methods developed in statistical physics to study economic systems. We analyze stock market indices and foreign exchange daily returns for 60 countries over the period of 1999-2012. We build a multi-layer network model based on different correlation measures, and introduce a dynamic network model to simulate and
Analytic device including nanostructures
Di Fabrizio, Enzo M.
2015-07-02
A device for detecting an analyte in a sample comprising: an array including a plurality of pixels, each pixel including a nanochain comprising: a first nanostructure, a second nanostructure, and a third nanostructure, wherein size of the first nanostructure is larger than that of the second nanostructure, and size of the second nanostructure is larger than that of the third nanostructure, and wherein the first nanostructure, the second nanostructure, and the third nanostructure are positioned on a substrate such that when the nanochain is excited by an energy, an optical field between the second nanostructure and the third nanostructure is stronger than an optical field between the first nanostructure and the second nanostructure, wherein the array is configured to receive a sample; and a detector arranged to collect spectral data from a plurality of pixels of the array.
Chemical space networks: a powerful new paradigm for the description of chemical space.
Maggiora, Gerald M; Bajorath, Jürgen
2014-08-01
The concept of chemical space is playing an increasingly important role in many areas of chemical research, especially medicinal chemistry and chemical biology. It is generally conceived as consisting of numerous compound clusters of varying sizes scattered throughout the space in much the same way as galaxies of stars inhabit our universe. A number of issues associated with this coordinate-based representation are discussed. Not the least of which is the continuous nature of the space, a feature not entirely compatible with the inherently discrete nature of chemical space. Cell-based representations, which are derived from coordinate-based spaces, have also been developed that facilitate a number of chemical informatic activities (e.g., diverse subset selection, filling 'diversity voids', and comparing compound collections).These representations generally suffer the 'curse of dimensionality'. In this work, networks are proposed as an attractive paradigm for representing chemical space since they circumvent many of the issues associated with coordinate- and cell-based representations, including the curse of dimensionality. In addition, their relational structure is entirely compatible with the intrinsic nature of chemical space. A description of the features of these chemical space networks is presented that emphasizes their statistical characteristics and indicates how they are related to various types of network topologies that exhibit random, scale-free, and/or 'small world' properties.
A pathway-based network analysis of hypertension-related genes
Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng
2016-02-01
Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.
Structure of global buyer-supplier networks and its implications for conflict minerals regulations
Mizuno, Takayuki; Watanabe, Tsutomu
2015-01-01
We investigate the structure of global inter-firm linkages using a dataset that contains information on business partners for about 400,000 firms worldwide, including all the firms listed on the major stock exchanges. Among the firms, we examine three networks, which are based on customer-supplier, licensee-licensor, and strategic alliance relationships. First, we show that these networks all have scale-free topology and that the degree distribution for each follows a power law with an exponent of 1.5. The shortest path length is around six for all three networks. Second, we show through community structure analysis that the firms comprise a community with those firms that belong to the same industry but different home countries, indicating the globalization of firms' production activities. Finally, we discuss what such production globalization implies for the proliferation of conflict minerals (i.e., minerals extracted from conflict zones and sold to firms in other countries to perpetuate fighting) through g...
Network connectivity modulates power spectrum scale invariance.
Rădulescu, Anca; Mujica-Parodi, Lilianne R
2014-04-15
Measures of complexity are sensitive in detecting disease, which has made them attractive candidates for diagnostic biomarkers; one complexity measure that has shown promise in fMRI is power spectrum scale invariance (PSSI). Even if scale-free features of neuroimaging turn out to be diagnostically useful, however, their underlying neurobiological basis is poorly understood. Using modeling and simulations of a schematic prefrontal-limbic meso-circuit, with excitatory and inhibitory networks of nodes, we present here a framework for how network density within a control system can affect the complexity of signal outputs. Our model demonstrates that scale-free behavior, similar to that observed in fMRI PSSI data, can be obtained for sufficiently large networks in a context as simple as a linear stochastic system of differential equations, although the scale-free range improves when introducing more realistic, nonlinear behavior in the system. PSSI values (reflective of complexity) vary as a function of both input type (excitatory, inhibitory) and input density (mean number of long-range connections, or strength), independent of their node-specific geometric distribution. Signals show pink noise (1/f) behavior when excitatory and inhibitory influences are balanced. As excitatory inputs are increased and decreased, signals shift towards white and brown noise, respectively. As inhibitory inputs are increased and decreased, signals shift towards brown and white noise, respectively. The results hold qualitatively at the hemodynamic scale, which we modeled by introducing a neurovascular component. Comparing hemodynamic simulation results to fMRI PSSI results from 96 individuals across a wide spectrum of anxiety-levels, we show how our model can generate concrete and testable hypotheses for understanding how connectivity affects regulation of meso-circuits in the brain. Copyright © 2013 Elsevier Inc. All rights reserved.
Information communication on complex networks
Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki
2013-02-01
Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.
Directory of Open Access Journals (Sweden)
Jonas Nesland Vevatne
2014-04-01
Full Text Available Fracturing and refreezing of sea ice in the Kara sea are investigated using complex networkanalysis. By going to the dual network, where the fractures are nodes and their intersectionslinks, we gain access to topological features which are easy to measure and hence comparewith modeled networks. Resulting network reveal statistical properties of the fracturing process.The dual networks have a broad degree distribution, with a scale-free tail, high clusteringand efficiency. The degree-degree correlation profile shows disassortative behavior, indicatingpreferential growth. This implies that long, dominating fractures appear earlier than shorterfractures, and that the short fractures which are created later tend to connect to the longfractures.The knowledge of the fracturing process is used to construct growing fracture network (GFNmodel which provides insight into the generation of fracture networks. The GFN model isprimarily based on the observation that fractures in sea ice are likely to end when hitting existingfractures. Based on an investigation of which fractures survive over time, a simple model forrefreezing is also added to the GFN model, and the model is analyzed and compared to the realnetworks.
Approximate entropy of network parameters
West, James; Lacasa, Lucas; Severini, Simone; Teschendorff, Andrew
2012-04-01
We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.
Rumor evolution in social networks
Zhang, Yichao; Zhou, Shi; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng
2013-03-01
The social network is a main tunnel of rumor spreading. Previous studies concentrated on a static rumor spreading. The content of the rumor is invariable during the whole spreading process. Indeed, the rumor evolves constantly in its spreading process, which grows shorter, more concise, more easily grasped, and told. In an early psychological experiment, researchers found about 70% of details in a rumor were lost in the first six mouth-to-mouth transmissions. Based on these observations, we investigate rumor spreading on social networks, where the content of the rumor is modified by the individuals with a certain probability. In the scenario, they have two choices, to forward or to modify. As a forwarder, an individual disseminates the rumor directly to their neighbors. As a modifier, conversely, an individual revises the rumor before spreading it out. When the rumor spreads on the social networks, for instance, scale-free networks and small-world networks, the majority of individuals actually are infected by the multirevised version of the rumor, if the modifiers dominate the networks. The individuals with more social connections have a higher probability to receive the original rumor. Our observation indicates that the original rumor may lose its influence in the spreading process. Similarly, a true information may turn out to be a rumor as well. Our result suggests the rumor evolution should not be a negligible question, which may provide a better understanding of the generation and destruction of a rumor.
Statistical Mechanics of Temporal and Interacting Networks
Zhao, Kun
In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide
Why are so many networks disassortative?
Johnson, Samuel; Torres, Joaquín J.; Marro, J.; Muñoz, Miguel A.
2011-03-01
A wide range of empirical networks—whether biological, technological, information-related or linguistic—generically exhibit important degree-degree anticorrelations (i.e., they are disassortative), the only exceptions being social ones, which tend to be positively correlated (assortative). Using an information-theory approach, we show that the equilibrium state of highly heterogeneous (scale-free) random networks is disassortative. This not only gives a parsimonious explanation to a long-standing question, but also provides a neutral model against which to compare experimental data and ascertain whether a given system is being driven from equilibrium by correlating mechanisms.
Modeling the spread of vector-borne diseases on bipartite networks.
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Donal Bisanzio
Full Text Available BACKGROUND: Vector-borne diseases for which transmission occurs exclusively between vectors and hosts can be modeled as spreading on a bipartite network. METHODOLOGY/PRINCIPAL FINDINGS: In such models the spreading of the disease strongly depends on the degree distribution of the two classes of nodes. It is sufficient for one of the classes to have a scale-free degree distribution with a slow enough decay for the network to have asymptotically vanishing epidemic threshold. Data on the distribution of Ixodes ricinus ticks on mice and lizards from two independent studies are well described by a scale-free distribution compatible with an asymptotically vanishing epidemic threshold. The commonly used negative binomial, instead, cannot describe the right tail of the empirical distribution. CONCLUSIONS/SIGNIFICANCE: The extreme aggregation of vectors on hosts, described by the power-law decay of the degree distribution, makes the epidemic threshold decrease with the size of the network and vanish asymptotically.
DEFF Research Database (Denmark)
Samson, Audrey; Soon, Winnie
2015-01-01
This paper examines the notion of network affordance within the context of network art. Building on Gibson's theory (Gibson, 1979) we understand affordance as the perceived and actual parameters of a thing. We expand on Gaver's affordance of predictability (Gaver, 1996) to include ecological...... and computational parameters of unpredictability. We illustrate the notion of unpredictability by considering four specific works that were included in a network art exhibiton, SPEED SHOW [2.0] Hong Kong. The paper discusses how the artworks are contingent upon the parameteric relations (Parisi, 2013......), of the network. We introduce network affordance as a dynamic framework that could articulate the experienced tension arising from the (visible) symbolic representation of computational processes and its hidden occurrences. We base our proposal on the experience of both organising the SPEED SHOW and participating...
DEFF Research Database (Denmark)
Korzenevica, Marina
2016-01-01
Following the civil war of 1996–2006, there was a dramatic increase in the labor mobility of young men and the inclusion of young women in formal education, which led to the transformation of the political landscape of rural Nepal. Mobility and schooling represent a level of prestige that rural...... politics. It analyzes how formal education and mobility either challenge or reinforce traditional gendered norms which dictate a lowly position for young married women in the household and their absence from community politics. The article concludes that women are simultaneously excluded and included from...... people regard as a prerequisite for participating in local community politics. Based on a fieldwork in two villages of Panchthar district in eastern Nepal, this article explores how these changes strengthen or weaken women’s political agency and how this is reflected in their participation in community...
Agent-based modeling and network dynamics
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...
Introduction to computer networking
Robertazzi, Thomas G
2017-01-01
This book gives a broad look at both fundamental networking technology and new areas that support it and use it. It is a concise introduction to the most prominent, recent technological topics in computer networking. Topics include network technology such as wired and wireless networks, enabling technologies such as data centers, software defined networking, cloud and grid computing and applications such as networks on chips, space networking and network security. The accessible writing style and non-mathematical treatment makes this a useful book for the student, network and communications engineer, computer scientist and IT professional. • Features a concise, accessible treatment of computer networking, focusing on new technological topics; • Provides non-mathematical introduction to networks in their most common forms today;< • Includes new developments in switching, optical networks, WiFi, Bluetooth, LTE, 5G, and quantum cryptography.
Directory of Open Access Journals (Sweden)
Fuchun Ren
2015-01-01
Full Text Available Risk and resilience are important and challenging issues in complex network systems since a single failure may trigger a whole collapse of the systems due to cascading effect. New theories, models, and methods are urgently demanded to deal with this challenge. In this paper, a coupled map lattices (CML based approach is adopted to analyze the risk of cascading process in Watts-Strogatz (WS small-world network and Barabási and Albert (BA scale-free network, respectively. Then, to achieve an effective and robust system and provide guidance in countering the cascading failure, a modified CML model with recovery strategy factor is proposed. Numerical simulations are put forward based on small-world CML and scale-free CML. The simulation results reveal that appropriate recovery strategies would significantly improve the resilience of networks.
Phylodynamic analysis of a viral infection network
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Teiichiro eShiino
2012-07-01
Full Text Available Viral infections by sexual and droplet transmission routes typically spread through a complex host-to-host contact network. Clarifying the transmission network and epidemiological parameters affecting the variations and dynamics of a specific pathogen is a major issue in the control of infectious diseases. However, conventional methods such as interview and/or classical phylogenetic analysis of viral gene sequences have inherent limitations and often fail to detect infectious clusters and transmission connections. Recent improvements in computational environments now permit the analysis of large datasets. In addition, novel analytical methods have been developed that serve to infer the evolutionary dynamics of virus genetic diversity using sample date information and sequence data. This type of framework, termed phylodynamics, helps connect some of the missing links on viral transmission networks, which are often hard to detect by conventional methods of epidemiology. With sufficient number of sequences available, one can use this new inference method to estimate theoretical epidemiological parameters such as temporal distributions of the primary infection, fluctuation of the pathogen population size, basic reproductive number, and the mean time span of disease infectiousness. Transmission networks estimated by this framework often have the properties of a scale-free network, which are characteristic of infectious and social communication processes. Network analysis based on phylodynamics has alluded to various suggestions concerning the infection dynamics associated with a given community and/or risk behavior. In this review, I will summarize the current methods available for identifying the transmission network using phylogeny, and present an argument on the possibilities of applying the scale-free properties to these existing frameworks.
A directed network of Greek and Roman mythology
Choi, Yeon-Mu; Kim, Hyun-Joo
2007-08-01
We construct a directed network using a dictionary of Greek and Roman mythology in which the nodes represent the entries listed in the dictionary and we make directional links from an entry to other entries that appear in its explanatory part. We find that this network is clearly not a random network but a directed scale-free network in which the distributions of out-degree and in-degree follow a power-law with exponents γout≈3.0 and γin≈2.5, respectively. Also we measure several quantities which describe the topological properties of the network and compare it to that of other real networks.
Mandala Networks: ultra-small-world and highly sparse graphs
Sampaio Filho, Cesar I. N.; Moreira, André A.; Andrade, Roberto F. S.; Herrmann, Hans J.; Andrade, José S.
2015-03-01
The increasing demands in security and reliability of infrastructures call for the optimal design of their embedded complex networks topologies. The following question then arises: what is the optimal layout to fulfill best all the demands? Here we present a general solution for this problem with scale-free networks, like the Internet and airline networks. Precisely, we disclose a way to systematically construct networks which are robust against random failures. Furthermore, as the size of the network increases, its shortest path becomes asymptotically invariant and the density of links goes to zero, making it ultra-small world and highly sparse, respectively. The first property is ideal for communication and navigation purposes, while the second is interesting economically. Finally, we show that some simple changes on the original network formulation can lead to an improved topology against malicious attacks.
Robertazzi, Thomas
2012-01-01
Springer Brief Basics of Computer Networking provides a non-mathematical introduction to the world of networks. This book covers both technology for wired and wireless networks. Coverage includes transmission media, local area networks, wide area networks, and network security. Written in a very accessible style for the interested layman by the author of a widely used textbook with many years of experience explaining concepts to the beginner.
Yamamoto, Ken; Yamazaki, Yoshihiro
2014-10-01
This paper proposes a numerical model of the network of two-Chinese-character compound words (two-character network, for short). In this network, a Chinese character is a node and a two-Chinese-character compound word links two nodes. The basic framework of the model is that an important character gets many edges. As the importance of a character, we use the frequency of each character appearing in publications. The direction of edge is given according to a random number assigned to nodes. The network generated by the model is small-world and scale-free, and reproduces statistical properties in the actual two-character network quantitatively.
Modularity in the evolution of yeast protein interaction network.
Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun
2015-01-01
Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.
A complex network analysis of hypertension-related genes
Wang, Huan; Xu, Chuan-Yun; Hu, Jing-Bo; Cao, Ke-Fei
2014-01-01
In this paper, a network of hypertension-related genes is constructed by analyzing the correlations of gene expression data among the Dahl salt-sensitive rat and two consomic rat strains. The numerical calculations show that this sparse and assortative network has small-world and scale-free properties. Further, 16 key hub genes (Col4a1, Lcn2, Cdk4, etc.) are determined by introducing an integrated centrality and have been confirmed by biological/medical research to play important roles in hypertension.
A Topological Description of Hubs in Amino Acid Interaction Networks
Directory of Open Access Journals (Sweden)
Omar Gaci
2010-01-01
Full Text Available We represent proteins by amino acid interaction networks. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. Once we have compared this type of graphs to the general model of scale-free networks, we analyze the existence of nodes which highly interact, the hubs. We describe these nodes taking into account their position in the primary structure to study their apparition frequency in the folded proteins. Finally, we observe that their interaction level is a consequence of the general rules which govern the folding process.
Probing the Extent of Randomness in Protein Interaction Networks
2008-07-11
scale-free networks are born equal: the role of the seed graph in PPI network evolution. PLoS Comput Biol 3: e118. doi:10.1371/journal.pcbi.0030118. 57...from seeds [56]. In the degree-conserving degree-weighted (DCDW) model, each node is considered once, in a random order, and a set number of edges are...gene duplication in fungi . Nature 449: 54–61. 63. Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW (2002) Evolutionary rate in the protein
Infection Dynamics on Growing Networks
Lai, Ying-Cheng; Liu, Zonghua; Ye, Nong
We consider the entire spectrum of architectures for large, growing, and complex networks, ranging from being heterogeneous (scale-free) to homogeneous (random or small-world), and investigate the infection dynamics by using a realistic three-state epidemiological model. In this framework, a node can be in one of the three states: susceptible (S), infected (I), or refractory (R), and the populations in the three groups are approximately described by a set of nonlinear differential equations. Our heuristic analysis predicts that, (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected, and (2) heterogeneous networks are relatively more robust against spread of infection as compared with homogeneous networks. These are confirmed numerically. We have also considered the problem of deliberate immunization for preventing wide spread of infection, with the result that targeted immunization can be quite effective for heterogeneous networks. We believe these results are important for a host of problems in many areas of natural science and engineering, and in social sciences as well.
Quantum Google in a Complex Network
Paparo, Giuseppe Davide; Müller, Markus; Comellas, Francesc; Martin-Delgado, Miguel Angel
2013-01-01
We investigate the behaviour of the recently proposed Quantum PageRank algorithm, in large complex networks. We find that the algorithm is able to univocally reveal the underlying topology of the network and to identify and order the most relevant nodes. Furthermore, it is capable to clearly highlight the structure of secondary hubs and to resolve the degeneracy in importance of the low lying part of the list of rankings. The quantum algorithm displays an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance, as compared to the classical algorithm. We test the performance and confirm the listed features by applying it to real world examples from the WWW. Finally, we raise and partially address whether the increased sensitivity of the quantum algorithm persists under coordinated attacks in scale-free and random networks. PMID:24091980
Quantum Google in a Complex Network
Paparo, Giuseppe Davide; Müller, Markus; Comellas, Francesc; Martin-Delgado, Miguel Angel
2013-10-01
We investigate the behaviour of the recently proposed Quantum PageRank algorithm, in large complex networks. We find that the algorithm is able to univocally reveal the underlying topology of the network and to identify and order the most relevant nodes. Furthermore, it is capable to clearly highlight the structure of secondary hubs and to resolve the degeneracy in importance of the low lying part of the list of rankings. The quantum algorithm displays an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance, as compared to the classical algorithm. We test the performance and confirm the listed features by applying it to real world examples from the WWW. Finally, we raise and partially address whether the increased sensitivity of the quantum algorithm persists under coordinated attacks in scale-free and random networks.
Evolution of Controllability in Interbank Networks
Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido
2013-04-01
The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected ``hub'' institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.
Evolution of complex modular biological networks.
Hintze, Arend; Adami, Christoph
2008-02-01
Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.
Evolution of complex modular biological networks.
Directory of Open Access Journals (Sweden)
Arend Hintze
2008-02-01
Full Text Available Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.
TRENDS IN THE CONVERGENCE OF WIRELESS NETWORKS
Directory of Open Access Journals (Sweden)
Daniel SORA
2011-01-01
Full Text Available In today’s technological market, there are many types of networks. These networks include wireless personal area networks (WPANs, wireless local area networks (WLANs, wireless metropolitan area networks (WMANs, and cellular networks. A vision of a future convergence of networks envisaged for WPANs, WLANs, WiMax, and cellular networks is presented in this paper.
An efficient mathematically correct scale free CORDIC
Hachaïchi, Yassine; Lahbib, Younes
2016-01-01
In order to approximate transandental functions, several algorithms were proposed.Historically, polynomial interpolation, infinite series, $\\cdots$ and other$+,\\times, -$ and $/$ based algorithms were studied for this purpose.The CORDIC (COordinate Rotation DIgital Computer)introduced by Jack E. Volder in 1959, and generalized by J. S. Walther a few years later, is a hardware based algorithmfor the approximation of trigonometric, hyperbolic andlogarithmic functions.As a consequence, CORDIC is...
Donahue, Gary
2011-01-01
Pick up where certification exams leave off. With this practical, in-depth guide to the entire network infrastructure, you'll learn how to deal with real Cisco networks, rather than the hypothetical situations presented on exams like the CCNA. Network Warrior takes you step by step through the world of routers, switches, firewalls, and other technologies based on the author's extensive field experience. You'll find new content for MPLS, IPv6, VoIP, and wireless in this completely revised second edition, along with examples of Cisco Nexus 5000 and 7000 switches throughout. Topics include: An
Engineering technology for networks
Paul, Arthur S.; Benjamin, Norman
1991-01-01
Space Network (SN) modeling and evaluation are presented. The following tasks are included: Network Modeling (developing measures and metrics for SN, modeling of the Network Control Center (NCC), using knowledge acquired from the NCC to model the SNC, and modeling the SN); and Space Network Resource scheduling.
Complexity Characteristics of Currency Networks
Gorski, A. Z.; Drozdz, S.; Kwapien, J.; Oswiecimka, P.
2006-11-01
A large set of daily FOREX time series is analyzed. The corresponding correlation matrices (CM) are constructed for USD, EUR and PLN used as the base currencies. The triangle rule is interpreted as constraints reducing the number of independent returns. The CM spectrum is computed and compared with the cases of shuffled currencies and a fictitious random currency taken as a base currency. The Minimal Spanning Tree (MST) graphs are calculated and the clustering effects for strong currencies are found. It is shown that for MSTs the node rank has power like, scale free behavior. Finally, the scaling exponents are evaluated and found in the range analogous to those identified recently for various complex networks.
Directory of Open Access Journals (Sweden)
Jodi Dean
2010-02-01
Full Text Available This article sets out the idea of affective networks as a constitutive feature of communicative capitalism. It explores the circulation of intensities in contemporary information and communication networks, arguing that this circulation should be theorized in terms of the psychoanalytic notion of the drive. The article includes critical engagements with theorists such as Guy Debord, Jacques Lacan, Tiziana Terranova, and Slavoj Zizek.
PREFACE: Complex Networks: from Biology to Information Technology
Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.
2008-06-01
networks consists of an overview of recent studies on hierarchical networks of phase oscillators. By analysing the evolution of the synchronous dynamics, one can infer details about the underlying network topology. Thus a connection between the dynamical and topological properties of the system is established. The paper Network synchronisation: optimal and pessimal scale-free topologies by Donetti et al explores an optimisation algorithm to study the properties of optimally synchronisable unweighted networks with scale-free degree distribution. It is shown that optimisation leads to a tendency towards disassortativity while networks that are optimally 'un-synchronisable' have a highly assortative string-like structure. The paper Critical line in undirected Kauffman Boolean networks—the role of percolation by Fronczak and Fronczak demonstrates that the percolation underlying the process of damage spreading impacts the position of the critical line in random boolean networks. The critical line results from the fact that the ordered behaviour of small clusters shields the chaotic behaviour of the giant component. In Impact of the updating scheme on stationary states of networks, Radicchi et al explore an interpolation between synchronous and asynchronous updating in a one-dimensional chain of Ising spins to locate a phase transition between phases with an absorbing and a fluctuating stationary state. The properties of attractors in the yeast cell-cycle network are also shown to depend sensitively on the updating mode. As this last contribution shows, a large part of the theoretical activity in the field can be applied to the study of biological systems. The section Biological Applications brings together the following contributions: In Applying weighted network measures to microarray distance matrices, Ahnert et al present a new approach to the analysis of weighted networks, which provides a generalisation to any network measure defined on unweighted networks. The
Complex network analysis of literary and scientific texts
Grabska-Gradzinska, Iwona; Kwapien, Jaroslaw; Drozdz, Stanislaw
2012-01-01
We present results from our quantitative study of statistical and network properties of literary and scientific texts written in two languages: English and Polish. We show that Polish texts are described by the Zipf law with the scaling exponent smaller than the one for the English language. We also show that the scientific texts are typically characterized by the rank-frequency plots with relatively short range of power-law behavior as compared to the literary texts. We then transform the texts into their word-adjacency network representations and find another difference between the languages. For the majority of the literary texts in both languages, the corresponding networks revealed the scale-free structure, while this was not always the case for the scientific texts. However, all the network representations of texts were hierarchical. We do not observe any qualitative and quantitative difference between the languages. However, if we look at other network statistics like the clustering coefficient and the...
Contagion processes on urban bus networks in Indian cities
Chatterjee, Atanu; Jagannathan, Krishna
2015-01-01
Bus transportation is considered as one of the most convenient and cheapest modes of public transportation in Indian cities. Due to their cost-effectiveness and wide reachability, they help a significant portion of the human population in cities to reach their destinations every day. Although from a transportation point of view they have numerous advantages over other modes of public transportation, they also pose a serious threat of contagious diseases spreading throughout the city. The presence of numerous local spatial constraints makes the process and extent of epidemic spreading extremely difficult to predict. Also, majority of the studies have focused on the contagion processes on scale-free network topologies whereas, spatially-constrained real-world networks such as, bus networks exhibit a wide-spectrum of network topology. Therefore, we aim in this study to understand this complex dynamical process of epidemic outbreak and information diffusion on the bus networks for six different Indian cities usin...
Modeling and Analysis of New Products Diffusion on Heterogeneous Networks
Directory of Open Access Journals (Sweden)
Shuping Li
2014-01-01
Full Text Available We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.
Modeling the reemergence of information diffusion in social network
Yang, Dingda; Liao, Xiangwen; Shen, Huawei; Cheng, Xueqi; Chen, Guolong
2018-01-01
Information diffusion in networks is an important research topic in various fields. Existing studies either focus on modeling the process of information diffusion, e.g., independent cascade model and linear threshold model, or investigate information diffusion in networks with certain structural characteristics such as scale-free networks and small world networks. However, there are still several phenomena that have not been captured by existing information diffusion models. One of the prominent phenomena is the reemergence of information diffusion, i.e., a piece of information reemerges after the completion of its initial diffusion process. In this paper, we propose an optimized information diffusion model by introducing a new informed state into traditional susceptible-infected-removed model. We verify the proposed model via simulations in real-world social networks, and the results indicate that the model can reproduce the reemergence of information during the diffusion process.
Rendón de la Torre, Stephanie; Kalda, Jaan; Kitt, Robert; Engelbrecht, Jüri
2016-09-01
This paper presents the first topological analysis of the economic structure of an entire country based on payments data obtained from Swedbank. This data set is exclusive in its kind because around 80% of Estonia's bank transactions are done through Swedbank, hence, the economic structure of the country can be reconstructed. Scale-free networks are commonly observed in a wide array of different contexts such as nature and society. In this paper, the nodes are comprised by customers of the bank (legal entities) and the links are established by payments between these nodes. We study the scaling-free and structural properties of this network. We also describe its topology, components and behaviors. We show that this network shares typical structural characteristics known in other complex networks: degree distributions follow a power law, low clustering coefficient and low average shortest path length. We identify the key nodes of the network and perform simulations of resiliency against random and targeted attacks of the nodes with two different approaches. With this, we find that by identifying and studying the links between the nodes is possible to perform vulnerability analysis of the Estonian economy with respect to economic shocks.
A simple rule for the evolution of cooperation on graphs and social networks.
Ohtsuki, Hisashi; Hauert, Christoph; Lieberman, Erez; Nowak, Martin A
2006-05-25
A fundamental aspect of all biological systems is cooperation. Cooperative interactions are required for many levels of biological organization ranging from single cells to groups of animals. Human society is based to a large extent on mechanisms that promote cooperation. It is well known that in unstructured populations, natural selection favours defectors over cooperators. There is much current interest, however, in studying evolutionary games in structured populations and on graphs. These efforts recognize the fact that who-meets-whom is not random, but determined by spatial relationships or social networks. Here we describe a surprisingly simple rule that is a good approximation for all graphs that we have analysed, including cycles, spatial lattices, random regular graphs, random graphs and scale-free networks: natural selection favours cooperation, if the benefit of the altruistic act, b, divided by the cost, c, exceeds the average number of neighbours, k, which means b/c > k. In this case, cooperation can evolve as a consequence of 'social viscosity' even in the absence of reputation effects or strategic complexity.
Yao, Chenggui; Zhan, Meng; Shuai, Jianwei; Ma, Jun; Kurths, Jürgen
2017-12-01
It has been generally believed that both time delay and network structure could play a crucial role in determining collective dynamical behaviors in complex systems. In this work, we study the influence of coupling strength, time delay, and network topology on synchronization behavior in delay-coupled networks of chaotic pendulums. Interestingly, we find that the threshold value of the coupling strength for complete synchronization in such networks strongly depends on the time delay in the coupling, but appears to be insensitive to the network structure. This lack of sensitivity was numerically tested in several typical regular networks, such as different locally and globally coupled ones as well as in several complex networks, such as small-world and scale-free networks. Furthermore, we find that the emergence of a synchronous periodic state induced by time delay is of key importance for the complete synchronization.
Effects of traffic generation patterns on the robustness of complex networks
Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui
2018-02-01
Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.
$k$-core percolation on complex networks: Comparing random, localized and targeted attacks
Yuan, Xin; Stanley, H Eugene; Havlin, Shlomo
2016-01-01
The type of malicious attack inflicting on networks greatly influences their stability under ordinary percolation in which a node fails when it becomes disconnected from the giant component. Here we study its generalization, $k$-core percolation, in which a node fails when it loses connection to a threshold $k$ number of neighbors. We study and compare analytically and by numerical simulations of $k$-core percolation the stability of networks under random attacks (RA), localized attacks (LA) and targeted attacks (TA), respectively. By mapping a network under LA or TA into an equivalent network under RA, we find that in both single and interdependent networks, TA exerts the greatest damage to the core structure of a network. We also find that for Erd\\H{o}s-R\\'{e}nyi (ER) networks, LA and RA exert equal damage to the core structure whereas for scale-free (SF) networks, LA exerts much more damage than RA does to the core structure.
Dorjee, S; Revie, C W; Poljak, Z; McNab, W B; Sanchez, J
2013-10-01
Understanding contact networks are important for modelling and managing the spread and control of communicable diseases in populations. This study characterizes the swine shipment network of a multi-site production system in southwestern Ontario, Canada. Data were extracted from a company's database listing swine shipments among 251 swine farms, including 20 sow, 69 nursery and 162 finishing farms, for the 2-year period of 2006 to 2007. Several network metrics were generated. The number of shipments per week between pairs of farms ranged from 1 to 6. The medians (and ranges) of out-degree were: sow 6 (1-21), nursery 8 (0-25), and finishing 0 (0-4), over the entire 2-year study period. Corresponding estimates for in-degree of nursery and finishing farms were 3 (0-9) and 3 (0-12) respectively. Outgoing and incoming infection chains (OIC and IIC), were also measured. The medians (ranges) of the monthly OIC and IIC were 0 (0-8) and 0 (0-6), respectively, with very similar measures observed for 2-week intervals. Nursery farms exhibited high measures of centrality. This indicates that they pose greater risks of disease spread in the network. Therefore, they should be given a high priority for disease prevention and control measures affecting all age groups alike. The network demonstrated scale-free and small-world topologies as observed in other livestock shipment studies. This heterogeneity in contacts among farm types and network topologies should be incorporated in simulation models to improve their validity. In conclusion, this study provided useful epidemiological information and parameters for the control and modelling of disease spread among swine farms, for the first time from Ontario, Canada. Copyright © 2013 Elsevier B.V. All rights reserved.
SBA Network Components & Software Inventory
Small Business Administration — SBA’s Network Components & Software Inventory contains a complete inventory of all devices connected to SBA’s network including workstations, servers, routers,...
Fundamentals of Stochastic Networks
Ibe, Oliver C
2011-01-01
An interdisciplinary approach to understanding queueing and graphical networks In today's era of interdisciplinary studies and research activities, network models are becoming increasingly important in various areas where they have not regularly been used. Combining techniques from stochastic processes and graph theory to analyze the behavior of networks, Fundamentals of Stochastic Networks provides an interdisciplinary approach by including practical applications of these stochastic networks in various fields of study, from engineering and operations management to communications and the physi
Preferential attachment in the evolution of metabolic networks
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Elofsson Arne
2005-11-01
Full Text Available Abstract Background Many biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks. Results The connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in βγ-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment. Conclusion Here, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate
Neural Networks: Implementations and Applications
Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.
1996-01-01
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas
Biological impacts and context of network theory
Energy Technology Data Exchange (ETDEWEB)
Almaas, E
2007-01-05
Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.
Valenzuela, Jesus Felix; Legara, Erika Fille; Fu, Xiuju; Goh, Rick Siow Mong; de Souza, Robert; Monterola, Christopher
2014-04-01
We examine the robustness of centralized, landbound relief operations' capability to promptly reach areas affected by a disaster event from a network perspective. We initially look at two idealized road networks: a two-dimensional grid and a scale-free network, and compare them to an actual road network obtained from OpenStreetMap. We show that, from a node designated as the center for relief operations (a "relief center"), damage to a road network causes a substantial fraction of the other nodes (about 20% in the three networks we examined) to become initially inaccessible from any relief effort, although the remaining majority can still be reached readily. Furthermore, we show the presence of a threshold in the two idealized road networks but not in the real one. Below this threshold, all nodes can robustly be reached in a short span of time, and above it, not only the partitioning mentioned above sets in, but also the time needed to reach the nodes becomes susceptible to the amount of damage sustained by the road network. Under damage sustained by random segments of the network, this threshold is higher in the scale-free network compared to the grid, due to the robustness of the former against random attacks. Our results may be of importance in formulating contingency plans for the logistics of disaster relief operations.
Study on the complex network characteristics of urban road system based on GIS
Gao, Zhonghua; Chen, Zhenjie; Liu, Yongxue; Huang, Kang
2007-06-01
Urban road system is the basic bone of urban transportation and one of the most important factors that influent and controls the urban configuration. In this paper, an approach of modeling, analyzing and optimizing urban road system is described based on complex network theory and GIS technology. The urban road system is studied on three focuses: building the urban road network, modeling the computational procedures based on urban road networks and analyzing the urban road system of Changzhou City as the study case. The conclusion is that the urban road network is a scale-free network with small-world characteristic, and there is still space for development of the whole network as a small-world network, also the key road crosses should be kept expedite.
PageRank model of opinion formation on social networks
Kandiah, Vivek; Shepelyansky, Dima L.
2012-11-01
We propose the PageRank model of opinion formation and investigate its rich properties on real directed networks of the Universities of Cambridge and Oxford, LiveJournal, and Twitter. In this model, the opinion formation of linked electors is weighted with their PageRank probability. Such a probability is used by the Google search engine for ranking of web pages. We find that the society elite, corresponding to the top PageRank nodes, can impose its opinion on a significant fraction of the society. However, for a homogeneous distribution of two opinions, there exists a bistability range of opinions which depends on a conformist parameter characterizing the opinion formation. We find that the LiveJournal and Twitter networks have a stronger tendency to a totalitarian opinion formation than the university networks. We also analyze the Sznajd model generalized for scale-free networks with the weighted PageRank vote of electors.
Modeling structure and resilience of the dark network.
De Domenico, Manlio; Arenas, Alex
2017-02-01
While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.
The value of peripheral nodes in controlling multilayer networks
Zhang, Yan; Schweitzer, Frank
2015-01-01
We analyze the controllability of a two-layer network, where driver nodes can be chosen only from one layer. Each layer contains a scale-free network with directed links. The dynamics of nodes depends on the incoming links from other nodes (reputation dynamics). We find that the controllable part of the network is larger when choosing peripherial nodes to connect the two layers. The control is as efficient for peripherial nodes as driver nodes as it is for more central nodes. If we assume a cost to utilize nodes which is proportional to their degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network.
Heterogeneous delivering capability promotes traffic efficiency in complex networks
Zhu, Yan-Bo; Guan, Xiang-Min; Zhang, Xue-Jun
2015-12-01
Traffic is one of the most fundamental dynamical processes in networked systems. With the homogeneous delivery capability of nodes, the global dynamic routing strategy proposed by Ling et al. [Phys. Rev. E81, 016113 (2010)] adequately uses the dynamic information during the process and thus it can reach a quite high network capacity. In this paper, based on the global dynamic routing strategy, we proposed a heterogeneous delivery allocation strategy of nodes on scale-free networks with consideration of nodes degree. It is found that the network capacity as well as some other indexes reflecting transportation efficiency are further improved. Our work may be useful for the design of more efficient routing strategies in communication or transportation systems.
Modeling structure and resilience of the dark network
De Domenico, Manlio; Arenas, Alex
2017-02-01
While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.
Self-similarity of complex networks and hidden metric spaces.
Serrano, M Angeles; Krioukov, Dmitri; Boguñá, Marián
2008-02-22
We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.
Learning Networks, Networked Learning
Sloep, Peter; Berlanga, Adriana
2010-01-01
Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05
Mapping and discrimination of networks in the complexity-entropy plane
Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.
2017-10-01
Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g., transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.
Cooperation in the prisoner's dilemma game on tunable community networks
Liu, Penghui; Liu, Jing
2017-04-01
Community networks have attracted lots of attention as they widely exist in the real world and are essential to study properties of networks. As the game theory illustrates the competitive relationship among individuals, studying the iterated prisoner's dilemma games (PDG) on community networks is meaningful. In this paper, we focus on investigating the relationship between the cooperation level of community networks and that of their communities in the prisoner's dilemma games. With this purpose in mind, a type of tunable community networks whose communities inherit not only the scale-free property, but also the characteristic of adjustable cooperation level of Holme and Kim (HK) networks is designed. Both uniform and non-uniform community networks are investigated. We find out that cooperation enhancement of communities can improve the cooperation level of the whole networks. Moreover, simulation results indicate that a large community is a better choice than a small community to improve the cooperation level of the whole networks. Thus, improving the cooperation level of community networks can be divided into a number of sub-problems targeting at improving the cooperation level of individual communities, which can save the computation cost and deal with the problem of improving the cooperation level of huge community networks. Moreover, as the larger community is a better choice, it is reasonable to start with large communities, according to the greedy strategy when the number of nodes can participate in the enhancement is limited.
Distribution of shortest path lengths in a class of node duplication network models
Steinbock, Chanania; Biham, Ofer; Katzav, Eytan
2017-09-01
We present analytical results for the distribution of shortest path lengths (DSPL) in a network growth model which evolves by node duplication (ND). The model captures essential properties of the structure and growth dynamics of social networks, acquaintance networks, and scientific citation networks, where duplication mechanisms play a major role. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network, forming a link to the mother node, and with probability p to each one of its neighbors. The degree distribution of the resulting network turns out to follow a power-law distribution, thus the ND network is a scale-free network. To calculate the DSPL we derive a master equation for the time evolution of the probability Pt(L =ℓ ) , ℓ =1 ,2 ,⋯ , where L is the distance between a pair of nodes and t is the time. Finding an exact analytical solution of the master equation, we obtain a closed form expression for Pt(L =ℓ ) . The mean distance 〈L〉 t and the diameter Δt are found to scale like lnt , namely, the ND network is a small-world network. The variance of the DSPL is also found to scale like lnt . Interestingly, the mean distance and the diameter exhibit properties of a small-world network, rather than the ultrasmall-world network behavior observed in other scale-free networks, in which 〈L〉 t˜lnlnt .
Effects of behavioral patterns and network topology structures on Parrondo’s paradox
Ye, Ye; Cheong, Kang Hao; Cen, Yu-Wan; Xie, Neng-Gang
2016-11-01
A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.
Iedema, Rick; Verma, Raj; Wutzke, Sonia; Lyons, Nigel; McCaughan, Brian
2017-04-10
Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author's participation in network activities and discussions with the agency's staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency's implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of "centralised decentralisation", co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid - the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a "network of networks" through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture "strategic hybrids", and enhance the impact of network activities on health system reform. Social implications Network governance requires more
Directory of Open Access Journals (Sweden)
Rebaï Ahmed
2010-10-01
Full Text Available Abstract Background Nuclear receptors (NRs and Receptor tyrosine kinases (RTKs are essential proteins in many cellular processes and sequence variations in their genes have been reported to be involved in many diseases including cancer. Although crosstalk between RTK and NR signalling and their contribution to the development of endocrine regulated cancers have been areas of intense investigation, the direct coupling of their signalling pathways remains elusive. In our understanding of the role and function of nuclear receptors on the cell membrane the interactions between nuclear receptors and tyrosine kinase receptors deserve further attention. Results We constructed a human signalling network containing nuclear receptors and tyrosine kinase receptors that identified a network topology involving eleven highly connected hubs. We further developed an integrated knowledge database, denominated NR-RTK database dedicated to human RTKs and NRs and their vertebrate orthologs and their interactions. These interactions were inferred using computational tools and those supported by literature evidence are indicated. NR-RTK database contains links to other relevant resources and includes data on receptor ligands. It aims to provide a comprehensive interaction map that identifies complex dynamics and potential crosstalk involved. Availability: NR-RTK database is accessible at http://www.bioinfo-cbs.org/NR-RTK/ Conclusions We infer that the NR-RTK interaction network is scale-free topology. We also uncovered the key receptors mediating the signal transduction between these two types of receptors. Furthermore, NR-RTK database is expected to be useful for researchers working on various aspects of the molecular basis of signal transduction by RTKs and NRs. Reviewers This article was reviewed by Professor Paul Harrison (nominated by Dr. Mark Gerstein, Dr. Arcady Mushegian and Dr. Anthony Almudevar.
Google matrix, dynamical attractors, and Ulam networks.
Shepelyansky, D L; Zhirov, O V
2010-03-01
We study the properties of the Google matrix generated by a coarse-grained Perron-Frobenius operator of the Chirikov typical map with dissipation. The finite-size matrix approximant of this operator is constructed by the Ulam method. This method applied to the simple dynamical model generates directed Ulam networks with approximate scale-free scaling and characteristics being in certain features similar to those of the world wide web with approximate scale-free degree distributions as well as two characteristics similar to the web: a power-law decay in PageRank that mirrors the decay of PageRank on the world wide web and a sensitivity to the value alpha in PageRank. The simple dynamical attractors play here the role of popular websites with a strong concentration of PageRank. A variation in the Google parameter alpha or other parameters of the dynamical map can drive the PageRank of the Google matrix to a delocalized phase with a strange attractor where the Google search becomes inefficient.
CLIC expands to include the Southern Hemisphere
Roberto Cantoni
2010-01-01
Australia has recently joined the CLIC collaboration: the enlargement will bring new expertise and resources to the project, and is especially welcome in the wake of CERN budget redistributions following the recent adoption of the Medium Term Plan. The countries involved in CLIC collaboration With the signing of a Memorandum of Understanding on 26 August 2010, the ACAS network (Australian Collaboration for Accelerator Science) became the 40th member of in the multilateral CLIC collaboration making Australia the 22nd country to join the collaboration. “The new MoU was signed by the ACAS network, which includes the Australian Synchrotron and the University of Melbourne”, explains Jean-Pierre Delahaye, CLIC Study Leader. “Thanks to their expertise, the Australian institutes will contribute greatly to the CLIC damping rings and the two-beam test modules." Institutes from any country wishing to join the CLIC collaboration are invited to assume responsibility o...
Intelligent networked teleoperation control
Li, Zhijun; Su, Chun-Yi
2015-01-01
This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.
New scaling relation for information transfer in biological networks
Kim, Hyunju; Davies, Paul; Walker, Sara Imari
2015-01-01
We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883
Cooperation in networks where the learning environment differs from the interaction environment.
Directory of Open Access Journals (Sweden)
Jianlei Zhang
Full Text Available We study the evolution of cooperation in a structured population, combining insights from evolutionary game theory and the study of interaction networks. In earlier studies it has been shown that cooperation is difficult to achieve in homogeneous networks, but that cooperation can get established relatively easily when individuals differ largely concerning the number of their interaction partners, such as in scale-free networks. Most of these studies do, however, assume that individuals change their behaviour in response to information they receive on the payoffs of their interaction partners. In real-world situations, subjects do not only learn from their interaction partners, but also from other individuals (e.g. teachers, parents, or friends. Here we investigate the implications of such incongruences between the 'interaction network' and the 'learning network' for the evolution of cooperation in two paradigm examples, the Prisoner's Dilemma game (PDG and the Snowdrift game (SDG. Individual-based simulations and an analysis based on pair approximation both reveal that cooperation will be severely inhibited if the learning network is very different from the interaction network. If the two networks overlap, however, cooperation can get established even in case of considerable incongruence between the networks. The simulations confirm that cooperation gets established much more easily if the interaction network is scale-free rather than random-regular. The structure of the learning network has a similar but much weaker effect. Overall we conclude that the distinction between interaction and learning networks deserves more attention since incongruences between these networks can strongly affect both the course and outcome of the evolution of cooperation.
Stability and dynamical properties of material flow systems on random networks
Anand, K.; Galla, T.
2009-04-01
The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.
Energy-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Xiaojuan Luo
2012-01-01
Full Text Available Based on the complex network theory, a new topological evolving model is proposed. In the evolution of the topology of sensor networks, the energy-aware mechanism is taken into account, and the phenomenon of change of the link and node in the network is discussed. Theoretical analysis and numerical simulation are conducted to explore the topology characteristics and network performance with different node energy distribution. We find that node energy distribution has the weak effect on the degree distribution P(k that evolves into the scale-free state, nodes with more energy carry more connections, and degree correlation is nontrivial disassortative. Moreover, the results show that, when nodes energy is more heterogeneous, the network is better clustered and enjoys higher performance in terms of the network efficiency and the average path length for transmitting data.
Fuchun Ren; Tingdi Zhao; Hongli Wang
2015-01-01
Risk and resilience are important and challenging issues in complex network systems since a single failure may trigger a whole collapse of the systems due to cascading effect. New theories, models, and methods are urgently demanded to deal with this challenge. In this paper, a coupled map lattices (CML) based approach is adopted to analyze the risk of cascading process in Watts-Strogatz (WS) small-world network and Barabási and Albert (BA) scale-free network, respectively. Then, to achieve an...
Using networking and communications software in business
McBride, PK
2014-01-01
Using Networking and Communications Software in Business covers the importance of networks in a business firm, the benefits of computer communications within a firm, and the cost-benefit in putting up networks in businesses. The book is divided into six parts. Part I looks into the nature and varieties of networks, networking standards, and network software. Part II discusses the planning of a networked system, which includes analyzing the requirements for the network system, the hardware for the network, and network management. The installation of the network system and the network managemen
Kevin Curran; Michael Mc Hugh
2013-01-01
The rise of social networking has revolutionised how people communicate on a daily basis. In a world where more people are connecting to the internet, social networking services create an immediate communication link between users. Social networking sites provide multiple services which include emailing, instant messaging, uploading files, gaming and finding friends. Just as social networking has become a more popular method of communication in recent years, the ways in which people look afte...
Complex network analysis of brain functional connectivity under a multi-step cognitive task
Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun
2017-01-01
Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.
Generic patterns in the evolution of urban water networks: Evidence from a large Asian city
Krueger, Elisabeth; Klinkhamer, Christopher; Urich, Christian; Zhan, Xianyuan; Rao, P. Suresh C.
2017-03-01
We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades. These results indicate that generic mechanisms govern the networks' evolution, similar to those of scale-free networks found in nature. Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities. We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.
Generic patterns in the evolution of urban water networks: Evidence from a large Asian city.
Krueger, Elisabeth; Klinkhamer, Christopher; Urich, Christian; Zhan, Xianyuan; Rao, P Suresh C
2017-03-01
We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades. These results indicate that generic mechanisms govern the networks' evolution, similar to those of scale-free networks found in nature. Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities. We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.
Efficient rewirings for enhancing synchronizability of dynamical networks
Rad, Ali Ajdari; Jalili, Mahdi; Hasler, Martin
2008-09-01
In this paper, we present an algorithm for optimizing synchronizability of complex dynamical networks. Starting with an undirected and unweighted network, we end up with an undirected and unweighted network with the same number of nodes and edges having enhanced synchronizability. To this end, based on some network properties, rewirings, i.e., eliminating an edge and creating a new edge elsewhere, are performed iteratively avoiding always self-loops and multiple edges between the same nodes. We show that the method is able to enhance the synchronizability of networks of any size and topological properties in a small number of steps that scales with the network size. For numerical simulations, an optimization algorithm based on simulated annealing is used. Also, the evolution of different topological properties of the network such as distribution of node degree, node and edge betweenness centrality is tracked with the iteration steps. We use networks such as scale-free, Strogatz-Watts and random to start with and we show that regardless of the initial network, the final optimized network becomes homogeneous. In other words, in the network with high synchronizability, parameters, such as, degree, shortest distance, node, and edge betweenness centralities are almost homogeneously distributed. Also, parameters, such as, maximum node and edge betweenness centralities are small for the rewired network. Although we take the eigenratio of the Laplacian as the target function for optimization, we show that it is also possible to choose other appropriate target functions exhibiting almost the same performance. Furthermore, we show that even if the network is optimized taking into account another interpretation of synchronizability, i.e., synchronization cost, the optimal network has the same synchronization properties. Indeed, in networks with optimized synchronizability, different interpretations of synchronizability coincide. The optimized networks are Ramanujan graphs
Robustness in Weighted Networks with Cluster Structure
Directory of Open Access Journals (Sweden)
Yi Zheng
2014-01-01
Full Text Available The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.
An empirical study of Chinese language networks
Zhou, Shuigeng; Hu, Guobiao; Zhang, Zhongzhi; Guan, Jihong
2008-05-01
Chinese is spoken by the largest number of people in the world, and it is regarded as one of the most important languages. In this paper, we explore the statistical properties of Chinese language networks (CLNs) within the framework of complex network theory. Based on one of the largest Chinese corpora, i.e. People’s Daily Corpus, we construct two networks (CLN1 and CLN2) from two different respects, with Chinese words as nodes. In CLN1, a link between two nodes exists if they appear next to each other in at least one sentence; in CLN2, a link represents that two nodes appear simultaneously in a sentence. We show that both networks exhibit small-world effect, scale-free structure, hierarchical organization and disassortative mixing. These results indicate that in many topological aspects Chinese language shapes complex networks with organizing principles similar to other previously studied language systems, which shows that different languages may have some common characteristics in their evolution processes. We believe that our research may shed some new light into the Chinese language and find some potentially significant implications.
Rumor propagation with heterogeneous transmission in social networks
Vega-Oliveros, Didier A.; Costa, Luciano da F.; Rodrigues, Francisco A.
2017-02-01
Rumor models consider that information transmission occurs with the same probability between each pair of nodes. However, this assumption is not observed in social networks, which contain influential spreaders. To overcome this limitation, we assume that central individuals have a higher capacity to convince their neighbors than peripheral subjects. From extensive numerical simulations we find that spreading is improved in scale-free networks when the transmission probability is proportional to the PageRank, degree, and betweenness centrality. In addition, the results suggest that spreading can be controlled by adjusting the transmission probabilities of the most central nodes. Our results provide a conceptual framework for understanding the interplay between rumor propagation and heterogeneous transmission in social networks.
Priority diffusion model in lattices and complex networks.
Maragakis, Michalis; Carmi, Shai; ben-Avraham, Daniel; Havlin, Shlomo; Argyrakis, Panos
2008-02-01
We introduce a model for diffusion of two classes of particles (A and B ) with priority: where both species are present in the same site the motion of A's takes precedence over that of B's. This describes realistic situations in wireless and communication networks. In regular lattices the diffusion of the two species is normal, but the B particles are significantly slower due to the presence of the A particles. From the fraction of sites where the B particles can move freely, which we compute analytically, we derive the diffusion coefficients of the two species. In heterogeneous networks the fraction of sites where B's are free decreases exponentially with the degree of the sites. This, coupled with accumulation of particles in high-degree nodes, leads to trapping of the low priority particles in scale-free networks.
Theorizing Network-Centric Activity in Education
HaLevi, Andrew
2011-01-01
Networks and network-centric activity are increasingly prevalent in schools and school districts. In addition to ubiquitous social network tools like Facebook and Twitter, educational leaders deal with a wide variety of network organizational forms that include professional development, advocacy, informational networks and network-centric reforms.…
Spatially Distributed Social Complex Networks
Directory of Open Access Journals (Sweden)
Gerald F. Frasco
2014-01-01
Full Text Available We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat’s law for the rates of city growth (by population size, in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008.]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.
Spatially Distributed Social Complex Networks
Frasco, Gerald F.; Sun, Jie; Rozenfeld, Hernán D.; ben-Avraham, Daniel
2014-01-01
We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities) and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat's law for the rates of city growth (by population size), in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008).]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.
Interarrival times of message propagation on directed networks
Mihaljev, Tamara; de Arcangelis, Lucilla; Herrmann, Hans J.
2011-08-01
One of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets, networks of infected computers used to send viruses, unsolicited commercial emails (SPAM) or denial of service attacks. We map this problem to the propagation of multiple random walkers on directed networks and we evaluate the interarrival time distribution between successive walkers arriving at a target. We show that the temporal organization of this process, which models information propagation on unstructured peer to peer networks, has the same features as SPAM reaching a single user. We study the behavior of the message interarrival time distribution on three different network topologies using two different rules for sending messages. In all networks the propagation is not a pure Poisson process. It shows universal features on Poissonian networks and a more complex behavior on scale free networks. Results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies, by studying interarrival times at any node of the network.
The network analysis of urban streets: A dual approach
Porta, Sergio; Crucitti, Paolo; Latora, Vito
2006-09-01
The application of the network approach to the urban case poses several questions in terms of how to deal with metric distances, what kind of graph representation to use, what kind of measures to investigate, how to deepen the correlation between measures of the structure of the network and measures of the dynamics on the network, what are the possible contributions from the GIS community. In this paper, the author considers six cases of urban street networks characterized by different patterns and historical roots. The authors propose a representation of the street networks based firstly on a primal graph, where intersections are turned into nodes and streets into edges. In a second step, a dual graph, where streets are nodes and intersections are edges, is constructed by means of a generalization model named Intersection Continuity Negotiation, which allows to acknowledge the continuity of streets over a plurality of edges. Finally, the authors address a comparative study of some structural properties of the dual graphs, seeking significant similarities among clusters of cases. A wide set of network analysis techniques are implemented over the dual graph: in particular the authors show that the absence of any clue of assortativity differentiates urban street networks from other non-geographic systems and that most of the considered networks have a broad degree distribution typical of scale-free networks and exhibit small-world properties as well.
Interarrival times of message propagation on directed networks.
Mihaljev, Tamara; de Arcangelis, Lucilla; Herrmann, Hans J
2011-08-01
One of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets, networks of infected computers used to send viruses, unsolicited commercial emails (SPAM) or denial of service attacks. We map this problem to the propagation of multiple random walkers on directed networks and we evaluate the interarrival time distribution between successive walkers arriving at a target. We show that the temporal organization of this process, which models information propagation on unstructured peer to peer networks, has the same features as SPAM reaching a single user. We study the behavior of the message interarrival time distribution on three different network topologies using two different rules for sending messages. In all networks the propagation is not a pure Poisson process. It shows universal features on Poissonian networks and a more complex behavior on scale free networks. Results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies, by studying interarrival times at any node of the network.
DEFF Research Database (Denmark)
Mweu, Marshal M.; Fournié, Guillaume; Hisham Beshara Halasa, Tariq
2013-01-01
Social network analysis provides a valuable framework for understanding the dynamics of diseases on networks as well as a means for defining effective control measures. An understanding of the underlying contact pattern for a susceptible population is advisable before embarking on any strategy...... for disease control. The objective of this study was to characterise the network of Danish cattle movements over a 10-year period from 2000 to 2009 with a view to understanding: (1) cohesiveness of the network, (2) influential holdings and (3) structural vulnerability of the network.Network analyses of data...... involving all cattle movements in Denmark registered during the period of interest were performed. A total of 50,494 premises participated in 4,204,895 individual movements during the 10-year period. The results pointed to a predominantly scale-free structure of the network; though marked by small...
Energy efficiency in wireless networks
Jumira, Oswald
2013-01-01
The last decade has witnessed an unprecedented development and growth in global wireless communications systems, technologies and network "traffic" generated over network infrastructures.This book presents state-of-the-art energy-efficient techniques, designs and implementations that pertain to wireless communication networks such as cellular networks, wireless local area networks (WLANs) and wireless ad hoc networks (WAHNs) including mobile ad hoc networks (MANETs), and wireless sensor networks (WSNs) as they are deployed across the world to facilitate "always on" reliable high-speed
DEFF Research Database (Denmark)
Dickison, Mark; Magnani, Matteo; Rossi, Luca
Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate...... social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various...... research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer...
DEFF Research Database (Denmark)
Dickison, Mark; Magnani, Matteo; Rossi, Luca
social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various......Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate...... research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer...
Common and Unique Network Dynamics in Football Games
Yamamoto, Yuji; Yokoyama, Keiko
2011-01-01
The sport of football is played between two teams of eleven players each using a spherical ball. Each team strives to score by driving the ball into the opposing goal as the result of skillful interactions among players. Football can be regarded from the network perspective as a competitive relationship between two cooperative networks with a dynamic network topology and dynamic network node. Many complex large-scale networks have been shown to have topological properties in common, based on a small-world network and scale-free network models. However, the human dynamic movement pattern of this network has never been investigated in a real-world setting. Here, we show that the power law in degree distribution emerged in the passing behavior in the 2006 FIFA World Cup Final and an international “A” match in Japan, by describing players as vertices connected by links representing passes. The exponent values are similar to the typical values that occur in many real-world networks, which are in the range of , and are larger than that of a gene transcription network, . Furthermore, we reveal the stochastically switched dynamics of the hub player throughout the game as a unique feature in football games. It suggests that this feature could result not only in securing vulnerability against intentional attack, but also in a power law for self-organization. Our results suggest common and unique network dynamics of two competitive networks, compared with the large-scale networks that have previously been investigated in numerous works. Our findings may lead to improved resilience and survivability not only in biological networks, but also in communication networks. PMID:22216336
Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui
2018-01-01
Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are
Networks Technology Conference
Tasaki, Keiji K. (Editor)
1993-01-01
The papers included in these proceedings represent the most interesting and current topics being pursued by personnel at GSFC's Networks Division and supporting contractors involved in Space, Ground, and Deep Space Network (DSN) technical work. Although 29 papers are represented in the proceedings, only 12 were presented at the conference because of space and time limitations. The proceedings are organized according to five principal technical areas of interest to the Networks Division: Project Management; Network Operations; Network Control, Scheduling, and Monitoring; Modeling and Simulation; and Telecommunications Engineering.
Held, Gilbert
2005-01-01
Wireless mesh networking is a new technology that has the potential to revolutionize how we access the Internet and communicate with co-workers and friends. Wireless Mesh Networks examines the concept and explores its advantages over existing technologies. This book explores existing and future applications, and examines how some of the networking protocols operate.The text offers a detailed analysis of the significant problems affecting wireless mesh networking, including network scale issues, security, and radio frequency interference, and suggests actual and potential solutions for each pro
Loo, Boon Thau
2012-01-01
Declarative Networking is a programming methodology that enables developers to concisely specify network protocols and services, which are directly compiled to a dataflow framework that executes the specifications. Declarative networking proposes the use of a declarative query language for specifying and implementing network protocols, and employs a dataflow framework at runtime for communication and maintenance of network state. The primary goal of declarative networking is to greatly simplify the process of specifying, implementing, deploying and evolving a network design. In addition, decla
Li, Xiang; Cao, Lang
2009-12-01
In recent years, there has been a growing interest in studying the role of costly punishment in promoting altruistic behaviors among selfish individuals. Rejections in ultimatum bargaining as a metaphor exemplify costly punishment, where the division of a sum of resources proposed by one side may be rejected by the other side, and both sides get nothing. Under a setting of the network of contacts among players, we find that the largest Laplacian eigenvalue of the network determines the critical division of players’ proposals, below which pure punishers who never accept any offers will emerge as a phase transition in the system. The critical division of offers that predicts the emergence of costly punishment is termed as the selfishness tolerance of a network within evolutionary ultimatum game, and extensive numerical simulations on the data of the science collaboration network, and computer-generated small-world/scale-free networks support the analytical findings.
Inhomogeneous Long-Range Percolation for Real-Life Network Modeling
Directory of Open Access Journals (Sweden)
Philippe Deprez
2015-01-01
Full Text Available The study of random graphs has become very popular for real-life network modeling, such as social networks or financial networks. Inhomogeneous long-range percolation (or scale-free percolation on the lattice Zd, d ≥ 1, is a particular attractive example of a random graph model because it fulfills several stylized facts of real-life networks. For this model, various geometric properties, such as the percolation behavior, the degree distribution and graph distances, have been analyzed. In the present paper, we complement the picture of graph distances and we prove continuity of the percolation probability in the phase transition point. We also provide an illustration of the model connected to financial networks.
Mixed Bose-Fermi statistics: Kinetic equation and navigation through a network
Chekmarev, Sergei F.
2010-08-01
The conventional master equation is generalized to include Bose and Fermi moves. The obtained equation describes the time evolution of a system with mixed Bose-Fermi statistics which are controlled by the fraction of Fermi moves (α) ; the latter plays a role of an exploratory tendency (ET) factor. The theoretical consideration is illustrated with numerical results for navigation through a model scale-free network. Depending on the ET factor and the number of participants involved in the navigation (Ntot) , a broad variety of the behavior scenarios has been observed. The cases of Ntot=1 and Ntot≫1 have been found drastically different. Of particular interest is the case of Ntot≫1 , when the participants interfere with each other’s motion, which is characteristic of many real systems (urban traffic, Internet, epidemic spreading, etc.). It has been found that at αstate but an excessive intention to try detour routes (α→1) leads to a critical slowdown in their passage to this state. At the same time, if α=1 (solely Fermi moves, no possibility to return to a previously visited state), only one of the participants attains the target state but it does it much faster than when a single copy navigates through the network (Ntot=1) , i.e., this one optimizes the way to the target state at the expense of other participants. The procedure used to include Bose and Fermi moves in the master equation offers a framework for derivation of kinetic equations for more complex and general statistics.
Robustness of Dengue Complex Network under Targeted versus Random Attack
Directory of Open Access Journals (Sweden)
Hafiz Abid Mahmood Malik
2017-01-01
Full Text Available Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO, 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia.
Synchronization properties of heterogeneous neuronal networks with mixed excitability type.
Leone, Michael J; Schurter, Brandon N; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G
2015-03-01
We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.
Mitigate Cascading Failures on Networks using a Memetic Algorithm.
Tang, Xianglong; Liu, Jing; Hao, Xingxing
2016-12-09
Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed Rcf. Then, adopting Rcf as the objective function, we propose a new memetic algorithm (MA) named MA-Rcf to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-Rcf. We systematically investigate the effects of parameters on the performance of the MA-Rcf and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-Rcf can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms.
Network coding at different layers in wireless networks
2016-01-01
This book focuses on how to apply network coding at different layers in wireless networks – including MAC, routing, and TCP – with special focus on cognitive radio networks. It discusses how to select parameters in network coding (e.g., coding field, number of packets involved, and redundant information ration) in order to be suitable for the varying wireless environments. The book explores how to deploy network coding in MAC to improve network performance and examines joint network coding with opportunistic routing to improve the successful rate of routing. In regards to TCP and network coding, the text considers transport layer protocol working with network coding to overcome the transmission error rate, particularly with how to use the ACK feedback of TCP to enhance the efficiency of network coding. The book pertains to researchers and postgraduate students, especially whose interests are in opportunistic routing and TCP in cognitive radio networks.
Magnitude Characterization Using Complex Networks in Central Chile
Pasten, D.; Comte, D.; Munoz, V.
2013-12-01
Studies using complex networks are applied to many systems, like traffic, social networks, internet and earth science. In this work we make an analysis using complex networks applied to magnitude of seismicity in the central zone of Chile, we use the preferential attachment in order to construct a seismic network using local magnitudes and the hypocenters of a seismic data set in central Chile. In order to work with a complete catalogue in magnitude, the data associated with the linear part of the Gutenberg-Richter law, with magnitudes greater than 2.7, were taken. We then make a grid in space, so that each seismic event falls into a certain cell, depending on the location of its hypocenter. Now the network is constructed: the first node corresponds to the cell where the first seismic event occurs. The node has an associated number which is the magnitude of the event which occured in it, and a probability is assigned to the node. The probability is a nonlinear mapping of the magnitude (a Gaussian function was taken), so that nodes with lower magnitude events are more likely to be attached to. Each time a new node is added to the network, it is attached to the previous node which has the larger probability; the link is directed from the previous node to the new node. In this way, a directed network is constructed, with a ``preferential attachment''-like growth model, using the magnitudes as the parameter to determine the probability of attachment to future nodes. Several events could occur in the same node. In this case, the probability is calculated using the average of the magnitudes of the events occuring in that node. Once the directed network is finished, the corresponding undirected network is constructed, by making all links symmetric, and eliminating the loops which may appear when two events occur in the same cell. The resulting directed network is found to be scale free (with very low values of the power-law distribution exponent), whereas the undirected
Skjeltorp, Arne T
2006-01-01
The book reviews the synergism between various fields of research that are confronted with networks, such as genetic and metabolic networks, social networks, the Internet and ecological systems. In many cases, the interacting networks manifest so-called emergent properties that are not possessed by any of the individual components. This means that the detailed knowledge of the components is insufficient to describe the whole system. Recent work has indicated that networks in nature have so-called scale-free characteristics, and the associated dynamic network modelling shows unexpected results such as an amazing robustness against accidental failures. Modelling the signal transduction networks in bioprocesses as in living cells is a challenging interdisciplinary research area. It is now realized that the many features of molecular interaction networks within a cell are shared to a large degree by the other complex systems mentioned above, such as the Internet, computer chips and society. Thus knowledge gained ...
Temporal stability in human interaction networks
Fabbri, Renato; Fabbri, Ricardo; Antunes, Deborah Christina; Pisani, Marilia Mello; de Oliveira, Osvaldo Novais
2017-11-01
This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.
Model of Opinion Spreading in Social Networks
Kanovsky, Igor
2011-01-01
We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Kleinberg "the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors. If one of them has an opinion, the actor adopts opinion with some low probability, if two - with a higher probability. Opinion spreading was simulated on different real world social networks and similar random scale-free networks. The results show that small world structure has a crucial impact on tipping point time. The "0-1-2" effect causes a significant difference between ability of the actors to start opinion spreading. Actor is an influencer according to his topological position in the network.
Seven Deadliest Network Attacks
Prowell, Stacy; Borkin, Mike
2010-01-01
Do you need to keep up with the latest hacks, attacks, and exploits effecting networks? Then you need Seven Deadliest Network Attacks. This book pinpoints the most dangerous hacks and exploits specific to networks, laying out the anatomy of these attacks including how to make your system more secure. You will discover the best ways to defend against these vicious hacks with step-by-step instruction and learn techniques to make your computer and network impenetrable. Attacks detailed in this book include: Denial of Service War Dialing Penetration "Testing" Protocol Tunneling Spanning Tree At
Cognitive Dynamic Optical Networks
DEFF Research Database (Denmark)
de Miguel, Ignacio; Duran, Ramon J.; Jimenez, Tamara
2013-01-01
The use of cognition is a promising element for the control of heterogeneous optical networks. Not only are cognitive networks able to sense current network conditions and act according to them, but they also take into account the knowledge acquired through past experiences; that is, they include...... learning with the aim of improving performance. In this paper, we review the fundamentals of cognitive networks and focus on their application to the optical networking area. In particular, a number of cognitive network architectures proposed so far, as well as their associated supporting technologies......, are reviewed. Moreover, several applications, mainly developed in the framework of the EU FP7 Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) project, are also described....
Techniques for Modelling Network Security
Lech Gulbinovič
2012-01-01
The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropri...
DEFF Research Database (Denmark)
Bigras-Poulin, M.; Thompson, R.A.; Chriél, Mariann
2006-01-01
Trade patterns of animal movements in a specific industry are complex and difficult to study because there are many stakeholders, premises that are heterogeneously spread over the country, and a highly dynamic flow of animals exists among them. The Danish cattle industry was defined as a network...... premises, (3) the specific premise network, and (4) the overall industry network. When contagious animals are moved from one premise to another, then to a third and so forth, these movements create a path for potential transfer of pathogens. The paths within which pathogens are present identify...... the transmission risks. A network of animal movements should provide information about pathogen transmission and disease spread. The network of the Danish cattle industry network was a directed scale-free graph (the direction of a movement was known), with an in-degree power of 2 an out-degree power of 1...
Theory including future not excluded
DEFF Research Database (Denmark)
Nagao, K.; Nielsen, H.B.
2013-01-01
We study a complex action theory (CAT) whose path runs over not only past but also future. We show that, if we regard a matrix element defined in terms of the future state at time T and the past state at time TA as an expectation value in the CAT, then we are allowed to have the Heisenberg equation......, Ehrenfest's theorem, and the conserved probability current density. In addition,we showthat the expectation value at the present time t of a future-included theory for large T - t and large t - T corresponds to that of a future-not-included theory with a proper inner product for large t - T. Hence, the CAT...
Breaking Free with Wireless Networks.
Fleischman, John
2002-01-01
Discusses wireless local area networks (LANs) which typically consist of laptop computers that connect to fixed access points via infrared or radio signals. Topics include wide area networks; personal area networks; problems, including limitations of available bandwidth, interference, and security concerns; use in education; interoperability;…
Professional social networking.
Rowley, Robert D
2014-12-01
We review the current state of social communication between healthcare professionals, the role of consumer social networking, and some emerging technologies to address the gaps. In particular, the review covers (1) the current state of loose social networking for continuing medical education (CME) and other broadcast information dissemination; (2) social networking for business promotion; (3) social networking for peer collaboration, including simple communication as well as more robust data-centered collaboration around patient care; and (4) engaging patients on social platforms, including integrating consumer-originated data into the mix of healthcare data. We will see how, as the nature of healthcare delivery moves from the institution-centric way of tradition to a more social and networked ambulatory pattern that we see emerging today, the nature of health IT has also moved from enterprise-centric systems to more socially networked, cloud-based options.
The network researchers' network
DEFF Research Database (Denmark)
Henneberg, Stephan C.; Jiang, Zhizhong; Naudé, Peter
2009-01-01
The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987). In thi......The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...
Ding, Lin; Leung, Victor C. M.; Tan, Min-Sheng
2017-09-01
The robustness of complex networks against cascading failures has been of great interest, while most of the researchers have considered undirected networks. However, to be more realistic, a part of links of many real systems should be described as unidirectional. In this paper, by applying three link direction-determining (DD) strategies, the tolerance of cascading failures is investigated in various networks with both unidirectional and bidirectional links. By extending the utilization of a classical global betweenness method, we propose a new cascading model, taking into account the weights of nodes and the directions of links. Then, the effects of unidirectional links on the network robustness against cascaded attacks are examined under the global load-based distribution mechanism. The simulation results show that the link-directed methods could not always lead to the decrease of the network robustness as indicated in the previous studies. For small-world networks, these methods certainly make the network weaker. However, for scale-free networks, the network robustness can be significantly improved by the link-directed method, especially for the method with non-random DD strategies. These results are independent of the weight parameter of the nodes. Due to the strongly improved robustness and easy realization with low cost on networks, the method for enforcing proper links to the unidirectional ones may be useful for leading to insights into the control of cascading failures in real-world networks, like communication and transportation networks.
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
Handbook of networking & connectivity
McClain, Gary R
1994-01-01
Handbook of Networking & Connectivity focuses on connectivity standards in use, including hardware and software options. The book serves as a guide for solving specific problems that arise in designing and maintaining organizational networks.The selection first tackles open systems interconnection, guide to digital communications, and implementing TCP/IP in an SNA environment. Discussions focus on elimination of the SNA backbone, routing SNA over internets, connectionless versus connection-oriented networks, internet concepts, application program interfaces, basic principles of layering, proto
Information networks and links
Davydov, G. B.
The basic operating principles of a telecommunications network are described. The technical characteristics of three types of telecommunications network are discussed, including voice-based (telephone) systems; television networks; and data (digital) communications. Among the specific transmission and channeling systems described are: satellite relays; microwae relays; ground based microwave relays; and pulse code modulation; (PCM). The integration of world wide systems for coordinating voice data, and video communications is also discussed.
Early warning signs in social-ecological networks.
Suweis, Samir; D'Odorico, Paolo
2014-01-01
A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance--measured, for example, by the maximum element of the covariance matrix of the network--is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.
Clustering in large networks does not promote upstream reciprocity.
Directory of Open Access Journals (Sweden)
Naoki Masuda
Full Text Available Upstream reciprocity (also called generalized reciprocity is a putative mechanism for cooperation in social dilemma situations with which players help others when they are helped by somebody else. It is a type of indirect reciprocity. Although upstream reciprocity is often observed in experiments, most theories suggest that it is operative only when players form short cycles such as triangles, implying a small population size, or when it is combined with other mechanisms that promote cooperation on their own. An expectation is that real social networks, which are known to be full of triangles and other short cycles, may accommodate upstream reciprocity. In this study, I extend the upstream reciprocity game proposed for a directed cycle by Boyd and Richerson to the case of general networks. The model is not evolutionary and concerns the conditions under which the unanimity of cooperative players is a Nash equilibrium. I show that an abundance of triangles or other short cycles in a network does little to promote upstream reciprocity. Cooperation is less likely for a larger population size even if triangles are abundant in the network. In addition, in contrast to the results for evolutionary social dilemma games on networks, scale-free networks lead to less cooperation than networks with a homogeneous degree distribution.
Information spreading in complex networks with participation of independent spreaders
Ma, Kun; Li, Weihua; Guo, Quantong; Zheng, Xiaoqi; Zheng, Zhiming; Gao, Chao; Tang, Shaoting
2018-02-01
Information diffusion dynamics in complex networks is often modeled as a contagion process among neighbors which is analogous to epidemic diffusion. The attention of previous literature is mainly focused on epidemic diffusion within one network, which, however neglects the possible interactions between nodes beyond the underlying network. The disease can be transmitted to other nodes by other means without following the links in the focal network. Here we account for this phenomenon by introducing the independent spreaders in a susceptible-infectious-recovered contagion process. We derive the critical epidemic thresholds on Erdős-Rényi and scale-free networks as a function of infectious rate, recovery rate and the activeness of independent spreaders. We also present simulation results on ER and SF networks, as well as on a real-world email network. The result shows that the extent to which a disease can infect might be more far-reaching, than we can explain in terms of link contagion only. Besides, these results also help to explain how activeness of independent spreaders can affect the diffusion process, which can be used to explore many other dynamical processes.
Early warning signs in social-ecological networks.
Directory of Open Access Journals (Sweden)
Samir Suweis
Full Text Available A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance--measured, for example, by the maximum element of the covariance matrix of the network--is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.
A framework for including family health spillovers in economic evaluation
H. Al-Janabi (Hareth); N.J.A. van Exel (Job); W.B.F. Brouwer (Werner); J. Coast (Joanna)
2016-01-01
textabstractHealth care interventions may affect the health of patients' family networks. It has been suggested that these health spillovers? should be included in economic evaluation, but there is not a systematic method for doing this. In this article, we develop a framework for including health
Energy Technology Data Exchange (ETDEWEB)
Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)
2015-03-01
Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.
Properties of kinetic transition networks for atomic clusters and glassy solids.
Morgan, John W R; Mehta, Dhagash; Wales, David J
2017-09-27
A database of minima and transition states corresponds to a network where the minima represent nodes and the transition states correspond to edges between the pairs of minima they connect via steepest-descent paths. Here we construct networks for small clusters bound by the Morse potential for a selection of physically relevant parameters, in two and three dimensions. The properties of these unweighted and undirected networks are analysed to examine two features: whether they are small-world, where the shortest path between nodes involves only a small number or edges; and whether they are scale-free, having a degree distribution that follows a power law. Small-world character is present, but statistical tests show that a power law is not a good fit, so the networks are not scale-free. These results for clusters are compared with the corresponding properties for the molecular and atomic structural glass formers ortho-terphenyl and binary Lennard-Jones. These glassy systems do not show small-world properties, suggesting that such behaviour is linked to the structure-seeking landscapes of the Morse clusters.
The effect of network structure on innovation initiation process: an evolutionary dynamics approach
Jafari, Afshin; Zolfagharzadeh, Mohammad Mahdi; Mohammadi, Mehdi
2016-01-01
In this paper we have proposed a basic agent-based model based on evolutionary dynamics for investigating innovation initiation process. In our model we suppose each agent will represent a firm which is interacting with other firms through a given network structure. We consider a two-hit process for presenting a potentially successful innovation in this model and therefore at each time step each firm can be in on of three different stages which are respectively, Ordinary, Innovative, and Successful. We design different experiments in order to investigate how different interaction networks may affect the process of presenting a successful innovation to the market. In this experiments, we use five different network structures, i.e. Erd\\H{o}s and R\\'enyi, Ring Lattice, Small World, Scale-Free and Distance-Based networks. According to the results of the simulations, for less frequent innovations like radical innovation, local structures are showing a better performance comparing to Scale-Free and Erd\\H{o}s and R\\...
Wang, Qingyun; Zhang, Honghui; Chen, Guanrong
2012-12-01
We study the effect of heterogeneous neuron and information transmission delay on stochastic resonance of scale-free neuronal networks. For this purpose, we introduce the heterogeneity to the specified neuron with the highest degree. It is shown that in the absence of delay, an intermediate noise level can optimally assist spike firings of collective neurons so as to achieve stochastic resonance on scale-free neuronal networks for small and intermediate α(h), which plays a heterogeneous role. Maxima of stochastic resonance measure are enhanced as α(h) increases, which implies that the heterogeneity can improve stochastic resonance. However, as α(h) is beyond a certain large value, no obvious stochastic resonance can be observed. If the information transmission delay is introduced to neuronal networks, stochastic resonance is dramatically affected. In particular, the tuned information transmission delay can induce multiple stochastic resonance, which can be manifested as well-expressed maximum in the measure for stochastic resonance, appearing every multiple of one half of the subthreshold stimulus period. Furthermore, we can observe that stochastic resonance at odd multiple of one half of the subthreshold stimulus period is subharmonic, as opposed to the case of even multiple of one half of the subthreshold stimulus period. More interestingly, multiple stochastic resonance can also be improved by the suitable heterogeneous neuron. Presented results can provide good insights into the understanding of the heterogeneous neuron and information transmission delay on realistic neuronal networks.
Advertising and Irreversible Opinion Spreading in Complex Social Networks
Candia, Julián
Irreversible opinion spreading phenomena are studied on small-world and scale-free networks by means of the magnetic Eden model, a nonequilibrium kinetic model for the growth of binary mixtures in contact with a thermal bath. In this model, the opinion of an individual is affected by those of their acquaintances, but opinion changes (analogous to spin flips in an Ising-like model) are not allowed. We focus on the influence of advertising, which is represented by external magnetic fields. The interplay and competition between temperature and fields lead to order-disorder transitions, which are found to also depend on the link density and the topology of the complex network substrate. The effects of advertising campaigns with variable duration, as well as the best cost-effective strategies to achieve consensus within different scenarios, are also discussed.
Modelling tipping-point phenomena of scientific coauthorship networks
Xie, Zheng; Yi, Dongyun; Zhenzheng, Ouyang; Li, Jianping
2016-01-01
In a range of scientific coauthorship networks, tipping points are detected in degree distributions, correlations between degrees and local clustering coefficients, etc. The existence of those tipping points could be treated as a result of the diversity of collaboration behaviours in scientific field. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two typical collaboration behaviours, namely the behaviour of leaders and that of other members in research teams. The model successfully predicts the tipping points, as well as many common features of coauthorship networks. For example, it realizes a process of deriving the complex scale-free property from the simple yes/no experiments. Moreover, it gives a reasonable explanation for the emergence of tipping points by the difference of collaboration behaviours between leaders and other members, which emerges in the evolution of research teams. The evolution synthetically addresses typical factors of generating collabora...
Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián
2012-01-01
Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.
Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián
2012-01-01
Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688
Capacity Limit, Link Scheduling and Power Control in Wireless Networks
Zhou, Shan
2013-01-01
The rapid advancement of wireless technology has instigated the broad deployment of wireless networks. Different types of networks have been developed, including wireless sensor networks, mobile ad hoc networks, wireless local area networks, and cellular networks. These networks have different structures and applications, and require different…
Statistical analysis of the road network of India
Indian Academy of Sciences (India)
These include the airport network of China [1], the airport network of India [2], the world-wide airport network [3,4], the urban road networks [5] and the railway networks [6–10]. The topology studies of different spatial networks show different degree distributions. Power law degree distribution is seen for Indian airport network ...
Beyond Space For Spatial Networks
Expert, Paul; Blondel, Vincent D; Lambiotte, Renaud
2010-01-01
Many complex systems are organized in the form of a network embedded in space. Important examples include the physical Internet infrastucture, road networks, flight connections, brain functional networks and social networks. The effect of space on network topology has recently come under the spotlight because of the emergence of pervasive technologies based on geo-localization, which constantly fill databases with people's movements and thus reveal their trajectories and spatial behaviour. Extracting patterns and regularities from the resulting massive amount of human mobility data requires the development of appropriate tools for uncovering information in spatially-embedded networks. In contrast with most works that tend to apply standard network metrics to any type of network, we argue in this paper for a careful treatment of the constraints imposed by space on network topology. In particular, we focus on the problem of community detection and propose a modularity function adapted to spatial networks. We sh...
Comparison of ancient and modern Chinese based on complex weighted networks.
Directory of Open Access Journals (Sweden)
Xinru Cui
Full Text Available In this study, we compare statistical properties of ancient and modern Chinese within the framework of weighted complex networks. We examine two language networks based on different Chinese versions of the Records of the Grand Historian. The comparative results show that Zipf's law holds and that both networks are scale-free and disassortative. The interactivity and connectivity of the two networks lead us to expect that the modern Chinese text would have more phrases than the ancient Chinese one. Furthermore, by considering some of the topological and weighted quantities, we find that expressions in ancient Chinese are briefer than in modern Chinese. These observations indicate that the two languages might have different linguistic mechanisms and combinatorial natures, which we attribute to the stylistic differences and evolution of written Chinese.
Assortativity and leadership emergence from anti-preferential attachment in heterogeneous networks
Sendiña-Nadal, I; Wang, Z; Havlin, S; Boccaletti, S
2015-01-01
Many real-world networks exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Particularly in social networks, the contribution to the total assortativity varies with degree, featuring a distinctive peak slightly past the average degree. The way traditional models imprint assortativity on top of pre-defined topologies is via degree-preserving link permutations, which however destroy the particular graph's hierarchical traits of clustering. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties and tunable realistic assortativity. In our approach, two distinct populations of nodes are added to an initial network seed: one (the followers) that abides by usual preferential rules, and one (the potential leaders) connecting via anti-preferential attachments, i.e. selecting lower degree nodes for their initial links. The latter nodes come to develop a higher average degree, and convert eventually into the final hubs....
Degree-correlation, omniscience, and randomized immunization protocols in finite networks
Alm, Jeremy F
2016-01-01
Many naturally occurring networks have a power-law degree distribution as well as a non-zero degree correlation. Despite this, most studies analyzing the efficiency of immunization strategies in networks have concentrated only on power-law degree distribution and ignored degree correlation. This study looks specifically at the effect degree-correlation has on the efficiency of several immunization strategies in scale-free networks. Generally, we found that positive degree correlation raises the number of immunized individuals needed to stop the spread of the infection. Importantly, we found that in networks with positive degree correlation, immunization strategies that utilize knowledge of initial popularity actually perform worse on average than random immunization strategies.
Understanding telecommunications networks
Valdar, Andy
2017-01-01
A telecommunications network is an electronic system of links, nodes and the controls that govern their operations to allow voice and data transfer among users and devices. This fully revised, updated and expanded second edition covers all aspects of today's networks, including how they are planned, formed and operated.
National Research Council Canada - National Science Library
Krishnamachari, Bhaskar
2005-01-01
... by networking techniques across multiple layers. The topics covered include network deployment, localization, time synchronization, wireless radio characteristics, medium-access, topology control, routing, data-centric techniques, and transport protocols. Ideal for researchers and designers seeking to create new algorithms and protocols and enginee...
Netotea, Sergiu; Sundell, David; Street, Nathaniel R; Hvidsten, Torgeir R
2014-02-06
Divergence in gene regulation has emerged as a key mechanism underlying species differentiation. Comparative analysis of co-expression networks across species can reveal conservation and divergence in the regulation of genes. We inferred co-expression networks of A. thaliana, Populus spp. and O. sativa using state-of-the-art methods based on mutual information and context likelihood of relatedness, and conducted a comprehensive comparison of these networks across a range of co-expression thresholds. In addition to quantifying gene-gene link and network neighbourhood conservation, we also applied recent advancements in network analysis to do cross-species comparisons of network properties such as scale free characteristics and gene centrality as well as network motifs. We found that in all species the networks emerged as scale free only above a certain co-expression threshold, and that the high-centrality genes upholding this organization tended to be conserved. Network motifs, in particular the feed-forward loop, were found to be significantly enriched in specific functional subnetworks but where much less conserved across species than gene centrality. Although individual gene-gene co-expression had massively diverged, up to ~80% of the genes still had a significantly conserved network neighbourhood. For genes with multiple predicted orthologs, about half had one ortholog with conserved regulation and another ortholog with diverged or non-conserved regulation. Furthermore, the most sequence similar ortholog was not the one with the most conserved gene regulation in over half of the cases. We have provided a comprehensive analysis of gene regulation evolution in plants and built a web tool for Comparative analysis of Plant co-Expression networks (ComPlEx, http://complex.plantgenie.org/). The tool can be particularly useful for identifying the ortholog with the most conserved regulation among several sequence-similar alternatives and can thus be of practical
Heterogeneous cellular networks
Hu, Rose Qingyang
2013-01-01
A timely publication providing coverage of radio resource management, mobility management and standardization in heterogeneous cellular networks The topic of heterogeneous cellular networks has gained momentum in industry and the research community, attracting the attention of standardization bodies such as 3GPP LTE and IEEE 802.16j, whose objectives are looking into increasing the capacity and coverage of the cellular networks. This book focuses on recent progresses, covering the related topics including scenarios of heterogeneous network deployment, interference management i
Wang, Wei; Huang, Li; Liang, Xuedong
2018-01-06
This paper investigates the reliability of complex emergency logistics networks, as reliability is crucial to reducing environmental and public health losses in post-accident emergency rescues. Such networks' statistical characteristics are analyzed first. After the connected reliability and evaluation indices for complex emergency logistics networks are effectively defined, simulation analyses of network reliability are conducted under two different attack modes using a particular emergency logistics network as an example. The simulation analyses obtain the varying trends in emergency supply times and the ratio of effective nodes and validates the effects of network characteristics and different types of attacks on network reliability. The results demonstrate that this emergency logistics network is both a small-world and a scale-free network. When facing random attacks, the emergency logistics network steadily changes, whereas it is very fragile when facing selective attacks. Therefore, special attention should be paid to the protection of supply nodes and nodes with high connectivity. The simulation method provides a new tool for studying emergency logistics networks and a reference for similar studies.
Mechanisms and dynamics of cooperation and competition emergence in complex networked systems
Gianetto, David A.
Cooperative behavior is a pervasive phenomenon in human interactions and yet how it can evolve and become established, through the selfish process of natural selection, is an enduring puzzle. These behaviors emerge when agents interact in a structured manner; even so, the key structural factors that affect cooperation are not well understood. Moreover, the literature often considers cooperation a single attribute of primitive agents who do not react to environmental changes but real-world actors are more perceptive. The present work moves beyond these assumptions by evolving more realistic game participants, with memories of the past, on complex networks. Agents play repeated games with a three-part Markovian strategy that allows us to separate the cooperation phenomenon into trust, reciprocity, and forgiveness characteristics. Our results show that networks matter most when agents gain the most by acting in a selfish manner, irrespective of how much they may lose by cooperating; since the context provided by neighborhoods inhibits greedy impulses that agents otherwise succumb to in isolation. Network modularity is the most important driver of cooperation emergence in these high-stakes games. However, modularity fails to tell the complete story. Modular scale-free graphs impede cooperation when close coordination is required, partially due to the acyclic nature of scale-free network models. To achieve the highest cooperation in diverse social conditions, both high modularity, low connectivity within modules, and a rich network of long cycles become important. With these findings in hand, we study the influence of networks on coordination and competition within the federal health care insurance exchange. In this applied study, we show that systemic health care coordination is encouraged by the emergent insurance network. The network helps underpin the viability of the exchange and provides an environment of stronger competition once a critical-mass of insurers have
Robinson, Julie A.; Tate-Brown, Judy M.
2009-01-01
Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.
Directory of Open Access Journals (Sweden)
Kudĕlka Miloš
2015-06-01
Full Text Available Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.
Complex network theory, streamflow, and hydrometric monitoring system design
Halverson, M. J.; Fleming, S. W.
2015-07-01
Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.
Complex Network Analysis of Literary and Scientific Texts
Grabska-Gradzińska, Iwona; Kulig, Andrzej; Kwapień, Jarosław; Drożdż, Stanisław
2012-07-01
We present results from our quantitative study of statistical and network properties of literary and scientific texts written in two languages: English and Polish. We show that Polish texts are described by the Zipf law with the scaling exponent smaller than the one for the English language. We also show that the scientific texts are typically characterized by the rank-frequency plots with relatively short range of power-law behavior as compared to the literary texts. We then transform the texts into their word-adjacency network representations and find another difference between the languages. For the majority of the literary texts in both languages, the corresponding networks revealed the scale-free structure, while this was not always the case for the scientific texts. However, all the network representations of texts were hierarchical. We do not observe any qualitative and quantitative difference between the languages. However, if we look at other network statistics like the clustering coefficient and the average shortest path length, the English texts occur to possess more clustered structure than do the Polish ones. This result was attributed to differences in grammar of both languages, which was also indicated in the Zipf plots. All the texts, however, show network structure that differs from any of the Watts-Strögatz, the Barabási-Albert, and the Erdös-Rényi architectures.
Hybrid modeling and empirical analysis of automobile supply chain network
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.
Double Percolation Phase Transition in Clustered Complex Networks
Directory of Open Access Journals (Sweden)
Pol Colomer-de-Simón
2014-10-01
Full Text Available The internal organization of complex networks often has striking consequences on either their response to external perturbations or on their dynamical properties. In addition to small-world and scale-free properties, clustering is the most common topological characteristic observed in many real networked systems. In this paper, we report an extensive numerical study on the effects of clustering on the structural properties of complex networks. Strong clustering in heterogeneous networks induces the emergence of a core-periphery organization that has a critical effect on the percolation properties of the networks. We observe a novel double phase transition with an intermediate phase in which only the core of the network is percolated and a final phase in which the periphery percolates regardless of the core. This result implies breaking of the same symmetry at two different values of the control parameter, in stark contrast to the modern theory of continuous phase transitions. Inspired by this core-periphery organization, we introduce a simple model that allows us to analytically prove that such an anomalous phase transition is, in fact, possible.
CERN. Geneva
2003-01-01
The series will start with an historical introduction about what people saw as high performance message communication in their time and how that developed to the now to day known "standard computer network communication". It will be followed by a far more technical part that uses the High Performance Computer Network standards of the 90's, with 1 Gbit/sec systems as introduction for an in depth explanation of the three new 10 Gbit/s network and interconnect technology standards that exist already or emerge. If necessary for a good understanding some sidesteps will be included to explain important protocols as well as some necessary details of concerned Wide Area Network (WAN) standards details including some basics of wavelength multiplexing (DWDM). Some remarks will be made concerning the rapid expanding applications of networked storage.
Zheng, Jun; Ansari, Nirwan
2005-06-01
are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to: Optical access network architectures and protocols Passive optical networks (BPON, EPON, GPON, etc.) Active optical networks Multiple access control Multiservices and QoS provisioning Network survivability Field trials and standards Performance modeling and analysis
Jiang, Yuming
2009-01-01
This book is devoted to a comprehensive treatment of this important field, a grand challenge for future networking research. It covers results from the authors as well as other researchers. Topics covered include Independent Case Analysis and its applications.
The networking practices of businesswomen
Directory of Open Access Journals (Sweden)
Marlene Bogaards
2011-12-01
Full Text Available Purpose: The primary objective of this study was to investigate the networking practices of businesswomen in the Gauteng Province, South Africa. The study of businesswomen's networking practices can provide critical insight into the manner in which networking can assist businesswomen in personal and professional skills development. Problem investigated: An investigation was conducted into the manner in which the concepts of networking and social networking are perceived by businesswomen in Gauteng. The study further investigated the motivation behind businesswomen's networking practices and the contents of their social networks were explored. Design/methodology/approach: A non-probability purposive sample, followed by snowball sampling, was used to select businesswomen for in-depth interviews (n = 31. In-depth interviews were used as a qualitative method to investigate the networking practices of these participants.Findings: The results indicate that businesswomen have various perceptions regarding the concepts of networking and social networking. It was also found that businesswomen include certain types of people in their social networks and that they receive various business and personal benefits when their networking is focused (having a clear objective to networking. Originality/value: These findings provide a background to the manner in which businesswomen perceive networking and social networking. This can serve as a basis for training initiatives to raise businesswomen's awareness of networking and social networking. Aforementioned may also provide insight into the meaning this may hold for them in their work and personal lives (for example, obtaining insight into the manner in which networking can be used to obtain business and personal support. Information derived from this study can further be utilised by businesswomen to assist them in social network development. Conclusion: It was concluded from the study that businesswomen perceive
IT Department
2009-01-01
A site wide network maintenance has been scheduled for Saturday 28 February. Most of the network devices of the General Purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites along this day. This upgrade will not affect: the Computer centre itself, building 613, the Technical Network and the LHC experiments dedicated networks at the pits. Should you need more details on this intervention, please contact Netops by phone 74927 or email mailto:Netops@cern.ch. IT/CS Group
GS Department
2009-01-01
A site-wide network maintenance operation has been scheduled for Saturday 28 February. Most of the network devices of the general purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites throughout the day. This upgrade will not affect the Computer Centre itself, Building 613, the Technical Network and the LHC experiments, dedicated networks at the pits. For further details of this intervention, please contact Netops by phone 74927 or e-mail mailto:Netops@cern.ch. IT/CS Group
Energy Efficient Network Function Virtualization in 5G Networks
Al-Quzweeni, AN; El-Gorashi, TEH; Nonde, L; Elmirghani, JMH
2015-01-01
A Number of merits could be brought by network function virtualization (NFV) such as scalability, on demand allocation of resources, and the efficient utilization of network resources. In this paper, we introduce a framework for designing an energy efficient architecture for 5G mobile network function virtualization. In the proposed architecture, the main functionalities of the mobile core network which include the packet gateway (P-GW), serving gateway (S-GW), mobility management entity (MME...
Directory of Open Access Journals (Sweden)
Leach Sonia M
2008-06-01
Full Text Available Abstract Background Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins. Results Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the
Cascading failures in interdependent modular networks with partial random coupling preference
Tian, Meng; Wang, Xianpei; Dong, Zhengcheng; Zhu, Guowei; Long, Jiachuang; Dai, Dangdang; Zhang, Qilin
2017-10-01
Cascading failures have been widely analyzed in interdependent networks with different coupling preferences from microscopic and macroscopic perspectives in recent years. Plenty of real-world interdependent infrastructures, representing as interdependent networks, exhibit community structure, one of the most important mesoscopic structures, and partial coupling preferences, which can affect cascading failures in interdependent networks. In this paper, we propose the partial random coupling in communities, investigating cascading failures in interdependent modular scale-free networks under inner attacks and hub attacks. We mainly analyze the effects of the discoupling probability and the intermodular connection probability on cascading failures in interdependent networks. We find that increasing either the dicoupling probability or the intermodular connection probability can enhance the network robustness under both hub attacks and inner attacks. We also note that the community structure can prevent cascading failures spreading globally in entire interdependent networks. Finally, we obtain the result that if we want to efficiently improve the robustness of interdependent networks and reduce the protection cost, the intermodular connection probability should be protected preferentially, implying that improving the robustness of a single network is the fundamental method to enhance the robustness of the entire interdependent networks.
A Simulation Study for Emergency/Disaster Management by Applying Complex Networks Theory
Directory of Open Access Journals (Sweden)
Li Jin
2014-04-01
Full Text Available Earthquakes, hurricanes, flooding and terrorist attacks pose a severe threat to our society. What’s more, when such a disaster happens, it can spread in a wide range with ubiquitous presence of a large-scale networked system. Therefore, the emergency/disaster management faces new challenges that the decision-makers have extra difficulties in perceiving the disaster dynamic spreading processes under this networked environment. This study tries to use the complex networks theory to tackle this complexity and the result shows the theory is a promising approach to support disaster/emergency management by focusing on simulation experiments of small world networks and scale free networks. The theory can be used to capture and describe the evolution mechanism, evolution discipline and overall behavior of a networked system. In particular, the complex networks theory is very strong at analyzing the complexity and dynamical changes of a networked system, which can improve the situation awareness after a disaster has occurred and help perceive its dynamic process, which is very important for high-quality decision making. In addition, this study also shows the use of the complex networks theory can build a visualized process to track the dynamic spreading of a disaster in a networked system.
Huang, Lei; Liao, Li; Wu, Cathy H
2017-01-01
Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf-combining a gene duplication/neofunctionalization model and scale-free model-has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.
Multifractal analysis and topological properties of a new family of weighted Koch networks
Huang, Da-Wen; Yu, Zu-Guo; Anh, Vo
2017-03-01
Weighted complex networks, especially scale-free networks, which characterize real-life systems better than non-weighted networks, have attracted considerable interest in recent years. Studies on the multifractality of weighted complex networks are still to be undertaken. In this paper, inspired by the concepts of Koch networks and Koch island, we propose a new family of weighted Koch networks, and investigate their multifractal behavior and topological properties. We find some key topological properties of the new networks: their vertex cumulative strength has a power-law distribution; there is a power-law relationship between their topological degree and weight strength; the networks have a high weighted clustering coefficient of 0.41004 (which is independent of the scaling factor c) in the limit of large generation t; the second smallest eigenvalue μ2 and the maximum eigenvalue μn are approximated by quartic polynomials of the scaling factor c for the general Laplacian operator, while μ2 is approximately a quartic polynomial of c and μn= 1.5 for the normalized Laplacian operator. Then, we find that weighted koch networks are both fractal and multifractal, their fractal dimension is influenced by the scaling factor c. We also apply these analyses to six real-world networks, and find that the multifractality in three of them are strong.
Optical network design and planning
Simmons, Jane M
2014-01-01
This book takes a pragmatic approach to designing state-of-the-art optical networks for backbone, regional, and metro-core networks. Algorithms and methodologies related to routing, regeneration, wavelength assignment, subrate-traffic grooming, and protection are presented, with an emphasis on optical-bypass-enabled (or all-optical) networks. There are numerous case studies throughout the text to illustrate the concepts, using realistic networks and traffic sets. A full chapter of economic studies offers guidelines as to when and how optical-bypass technology should be deployed. There is also extensive coverage of recent research to provide insight into how optical networks are likely to evolve. The second edition includes new chapters on dynamic optical networking and flexible/elastic optical networks. There is expanded coverage of new physical-layer technology and its impact on network design, along with enhanced coverage of ROADM architectures, including the colorless, directionless, contentionless, a...
Directory of Open Access Journals (Sweden)
Patrick Jagoda
2015-08-01
Full Text Available The language of networks now describes everything from the Internet to the economy to terrorist organizations. In distinction to a common view of networks as a universal, originary, or necessary form that promises to explain everything from neural structures to online traffic, this essay emphasizes the contingency of the network imaginary. Network form, in its role as our current cultural dominant, makes scarcely imaginable the possibility of an alternative or an outside uninflected by networks. If so many things and relationships are figured as networks, however, then what is not a network? If a network points towards particular logics and qualities of relation in our historical present, what others might we envision in the future? In many ways, these questions are unanswerable from within the contemporary moment. Instead of seeking an avant-garde approach (to move beyond networks or opting out of networks (in some cases, to recover elements of pre-networked existence, this essay proposes a third orientation: one of ambivalence that operates as a mode of extreme presence. I propose the concept of "network aesthetics," which can be tracked across artistic media and cultural forms, as a model, style, and pedagogy for approaching interconnection in the twenty-first century. The following essay is excerpted from Network Ambivalence (Forthcoming from University of Chicago Press.
Complex networks, streamflow, and hydrometric monitoring system design
Halverson, M.; Fleming, S.
2014-12-01
Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, has a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the results did not clearly suggest a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A community detection algorithm identified 10 separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Betweenness analyses additionally suggest a handful of key stations which serve as bridges between communities and might therefore be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, as well as small-membership communities which are by definition rare or undersampled relative to other communities, while retaining some degree of redundancy to maintain network robustness.
CNEM: Cluster Based Network Evolution Model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
Including Students with Severe Disabilities in General Education Settings.
Wisniewski, Lech; Alper, Sandra
1994-01-01
This paper presents five systematic phases for bringing about successful regular education inclusion of students with severe disabilities. Phases include develop networks within the community, assess school and community resources, review strategies for integration, install strategies that lead to integration, and develop a system of feedback and…
Simulation Of Networking Protocols On Software Emulated Network Stack
Directory of Open Access Journals (Sweden)
Hrushikesh Nimkar
2015-08-01
Full Text Available With the increasing number and complexity of network based applications the need to easy configuration development and integration of network applications has taken a high precedence. Trivial activities such as configuration can be carried out efficiently if network services are software based rather than hardware based. Project aims at enabling the network engineers to easily include network functionalities into hisher configuration and define hisher own network stack without using the kernel network stack. Having thought of this we have implemented two functionalities UPNP and MDNS. The multicast Domain Name System MDNS resolves host names to IP addresses within small ad-hoc networks and without having need of special DNS server and its configuration. MDNS application provides every host with functionality to register itself to the router make a multicast DNS request and its resolution. To make adding network devices and networked programs to a network as easy as it is to plug in a piece of hardware into a PC we make use of UPnP. The devices and programs find out about the network setup and other networked devices and programs through discovery and advertisements of services and configure themselves accordingly. UPNP application provides every host with functionality of discovering services of other hosts and serving requests on demand. To implement these applications we have used snabbswitch framework which an open source virtualized ethernet networking stack.
Seven Deadliest Social Network Attacks
Timm, Carl
2010-01-01
Do you need to keep up with the latest hacks, attacks, and exploits effecting social networks? Then you need Seven Deadliest Social Network Attacks. This book pinpoints the most dangerous hacks and exploits specific to social networks like Facebook, Twitter, and MySpace, laying out the anatomy of these attacks including how to make your system more secure. You will discover the best ways to defend against these vicious hacks with step-by-step instruction and learn techniques to make your computer and network impenetrable. Attacks detailed in this book include: Social Networking Infrastruct
McKee, Shawn; ATLAS Collaboration
2017-10-01
Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks. We will report on a number of networking initiatives in ATLAS including participation in the global perfSONAR network monitoring and measuring efforts of WLCG and OSG, the collaboration with the LHCOPN/LHCONE effort, the integration of network awareness into PanDA, the use of the evolving ATLAS analytics framework to better understand our networks and the changes in our DDM system to allow remote access to data. We will also discuss new efforts underway that are exploring the inclusion and use of software defined networks (SDN) and how ATLAS might benefit from: • Orchestration and optimization of distributed data access and data movement. • Better control of workflows, end to end. • Enabling prioritization of time-critical vs normal tasks • Improvements in the efficiency of resource usage
DEFF Research Database (Denmark)
Grande, Bård; Sørensen, Ole Henning
1998-01-01
The paper focuses on the concept of organizational networks. Four different uses of the concept of organizational network are identified and critically discussed. Special focus is placed on how information and communication technologies as communication mediators and cognitive pictures influence...