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Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes. Batool K, Niazi MA (2014) Towards a Methodology for Validation of Centrality Measures in Complex Networks. PLoS ONE 9(4): e90283. http://dx.doi.org/10.1371/journal.pone.0090283
Via Complexity Digest
We cannot say we weren't warned. The growing threat of antibiotic resistant organisms is once again in the spotlight. Prof Jeremy Farrar, the new head of Britain's biggest medical research charity the Wellcome Trust said it was a "truly global issue". In his first major interview since taking up his post, Prof Farrar told BBC Radio 4's Today programme that the golden age of antibiotics could come to an end unless action is taken
Many residents of Britain, Italy, and Belgium imagine there to be a kind of northsouth divide in their countries, marking a barrier between different social groups and regional characteristics. Now a new study by MIT researchers reveals that such divides can be seen in the patterns of communication in those countries and others.
Via Claudia Mihai, Complexity Institute
Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as `skeletons' or `backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario. Robustness of skeletons and salient features in networks Louis M. Shekhtman, James P. Bagrow, Dirk Brockmann http://arxiv.org/abs/1309.3797
Via Complexity Digest, Eugene Ch'ng
Ever notice how ant colonies so successfully explore food at 4th of July picnics? It’s all done without any central control.
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 facetoface 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 lowflow links are more represented than the same cliques with all highflow links. The representativity of these lowflow 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 scalefree, smallworld, ultra smallworld, Deltadistribution, compact, fractal, regular and random networks, and by revising this model, we show that one can produce communitystructure 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

Finding the most efficient way to transport items across a network like the U.S. highway system or the Internet is a problem that has taxed mathematicians and computer scientists for decades. To tackle the problem, researchers have traditionally used a maximumflow algorithm, also known as “max flow,” in which a network is represented as a graph with a series of nodes, known as vertices, and connecting lines between them, called edges.
Describing a social network based on a particular type of human social interaction, say, Facebook, is conceptually simple: a set of nodes representing the people involved in such a network, linked by their Facebook connections. But, what kind of network structure would one have if all modes of social interactions between the same people are taken into account and if one mode of interaction can influence another? Here, the notion of a “multiplex” network becomes necessary. Indeed, the scientific interest in multiplex networks has recently seen a surge. However, a fundamental scientific language that can be used consistently and broadly across the many disciplines that are involved in complex systems research was still missing. This absence is a major obstacle to further progress in this topical area of current interest. In this paper, we develop such a language, employing the concept of tensors that is widely used to describe a multitude of degrees of freedom associated with a single entity. Our tensorial formalism provides a unified framework that makes it possible to describe both traditional “monoplex” (i.e., singletype links) and multiplex networks. Each type of interaction between the nodes is described by a singlelayer network. The different modes of interaction are then described by different layers of networks. But, a node from one layer can be linked to another node in any other layer, leading to “cross talks” between the layers. Highdimensional tensors naturally capture such multidimensional patterns of connectivity. Having first developed a rigorous tensorial definition of such multilayer structures, we have also used it to generalize the many important diagnostic concepts previously known only to traditional monoplex networks, including degree centrality, clustering coefficients, and modularity. We think that the conceptual simplicity and the fundamental rigor of our formalism will power the further development of our understanding of multiplex networks.
Via Claudia Mihai, Complexity Institute
Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive realworld networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several realworld networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
Via Claudia Mihai, Complexity Digest, Frédéric Amblard
In a general sense, a Markov Network Brain (MNB) implements a probabilistic finite state machine, and as such is a Hidden Markov Model (HMM). MNBs act as controllers and decision makers for agents ...
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, systemscale 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 FerreriCancho, Romualdo PastorSatorras, 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 timedependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semidecomposable 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 timedependent 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

Love this stuff.