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Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the cognitive sciences. Networks in Cognitive Science Andrea Baronchelli, Ramon Ferrer-i-Cancho, Romualdo Pastor-Satorras, Nick Chater, Morten H. Christiansen http://arxiv.org/abs/1304.6736
Via Complexity Digest
We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (“optimization variance”) and over randomizations of network structure (“randomization variance”). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data. Robust detection of dynamic community structure in networks
Danielle S. Bassett, Mason A. Porter, Nicholas F. Wymbs, Scott T. Grafton, Jean M. Carlson, and Peter J. Mucha Chaos 23, 013142 (2013); http://dx.doi.org/10.1063/1.4790830 ;
Via Complexity Digest, Eugene Ch'ng
Social network analysis is now widely used to investigate the dynamics of infectious disease spread from person to person. Vaccination dramatically disrupts the disease transmission process on a contact network, and indeed, sufficiently high vaccination rates can disrupt the process to such an extent that disease transmission on the network is effectively halted. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread through social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model both the health behavior and the infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This variability in dynamics results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks. Complex social contagion makes networks more vulnerable to disease outbreaks Ellsworth Campbell, Marcel Salathé http://arxiv.org/abs/1211.0518
Via Complexity Digest
A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system’s state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive. Thermodynamics of Prediction Susanne Still, David A. Sivak, Anthony J. Bell, and Gavin E. Crooks Phys. Rev. Lett. 109, 120604 (2012) http://dx.doi.org/10.1103/PhysRevLett.109.120604
Via Complexity Digest
Big fan of RSA Animate and given we deal wtih networked systems everyday this episode was just awesome. In this RSA Animate, Manuel Lima senior UX design lead at Microsoft Bing, explores the power of network visualisation to help navigate our complex modern world. Click on the image or the title to learn more.
Via FastTFriend, Complexity Institute, Eugene Ch'ng
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Networks are commonly used to define underlying interaction structures where infections, information, or other quantities may spread. Although the standard approach has been to aggregate all links into a static structure, some studies have shown that the time order in which the links are established may alter the dynamics of spreading. In this paper, we study the impact of the time ordering in the limits of flow on various empirical temporal networks. By using a random walk dynamics, we estimate the flow on links and convert the original undirected network (temporal and static) into a directed flow network. We then introduce the concept of flow motifs and quantify the divergence in the representativity of motifs when using the temporal and static frameworks. We find that the regularity of contacts and persistence of vertices (common in email communication and face-to-face interactions) result on little differences in the limits of flow for both frameworks. On the other hand, in the case of communication within a dating site and of a sexual network, the flow between vertices changes significantly in the temporal framework such that the static approximation poorly represents the structure of contacts. We have also observed that cliques with 3 and 4 vertices containing only low-flow links are more represented than the same cliques with all high-flow links. The representativity of these low-flow cliques is higher in the temporal framework. Our results suggest that the flow between vertices connected in cliques depend on the topological context in which they are placed and in the time sequence in which the links are established. The structure of the clique alone does not completely characterize the potential of flow between the vertices. Flow motifs reveal limitations of the static framework to represent human interactions Luis E. C. Rocha and Vincent D. Blondel Phys. Rev. E 87, 042814 (2013) http://dx.doi.org/10.1103/PhysRevE.87.042814
Via Complexity Digest
Talk at Understanding Financial Catastrophe Risk: Developing a Research Agenda.
DTNP is go... Astronauts on the International Space Station use an experimental version of interplanetary internet (more correctly the Disruption Tolerant Networking (DTN) Protocol) to control a robot on Earth. Vint Cerf proposed this the basis of DTN over 10 years ago and the demonstration shows how communciations using the store and forward protocol are increasingly possible over very large distances. (a previous test was conducted with image transmition between earth and a remote satellite, 20 million miles away.). Click on the image or title to learn more via the BBC Technologiy article.
Researchers have discovered many types of complex networks and have proposed hundreds of models to explain their origins, yet most of the relationships within each of these types are still uncertain. Furthermore, because of the large number of types and models of complex networks, it is widely thought that these complex networks cannot all share a simple universal explanation. However, here we find that a simple model can produce many types of complex networks, including scale-free, small-world, ultra small-world, Delta-distribution, compact, fractal, regular and random networks, and by revising this model, we show that one can produce community-structure networks. Using this model and its revised versions, the complicated relationships among complex networks can be illustrated. Given that complex networks are regarded as a model tool of complex systems, the results here bring a new perspective to understanding the power law phenomena observed in various complex systems. A simple model clarifies the complicated relationships of complex networks Bojin Zheng, Hongrun Wu, Jun Qin, Wenhua Du, Jianmin Wang, Deyi Li http://arxiv.org/abs/1210.3121
Via Complexity Digest, Eugene Ch'ng
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