***** To join INSNA, visit http://www.insna.org ***** A little late this time--the Digest mailing went out later than usual, which threw off my schedule. Apologies! In any case, there's some interesting stuff in this edition which will hopefully make up for the delay. Dawn ====================== Dynamics in online social networks Przemyslaw A. Grabowicz, Jose J. Ramasco, Victor M. Eguiluz http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=95501a9a28&e=d38efa683e In this chapter we describe some of the results of research studies on the structure, dynamics and social activity in online social networks. We present them in the interdisciplinary context of network science, sociological studies and computer science. ---------------------------- A simple model clarifies the complicated relationships of complex networks Bojin Zheng, Hongrun Wu, Jun Qin, Wenhua Du, Jianmin Wang, Deyi Li http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=1037e27f27&e=d38efa683e 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. ----------------------------- Control Centrality and Hierarchical Structure in Complex Networks Liu Y-Y, Slotine J-J, Barabási A-L (2012) PLoS ONE 7(9): e44459. http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7d4a593e5f&e=d38efa683e We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks. ----------------------------- Robustness and Information Propagation in Attractors of Random Boolean NetworksLloyd-Price J, Gupta A, Ribeiro AS (2012) PLoS ONE 7(7): e42018. doi:10.1371/journal.pone.0042018 http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b2d8279545&e=d38efa683e Attractors represent the long-term behaviors of Random Boolean Networks. We study how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information (I_A), relates to the robustness of the attractor to perturbations (R_A). We find that the dynamical regime of the network affects the relationship between I_A and R_A. In the ordered and chaotic regimes, I_A is anti-correlated with R_A, implying that attractors that are highly robust to perturbations have necessarily limited information propagation. Between order and chaos (for so-called “critical” networks) these quantities are uncorrelated. Finite size effects cause this behavior to be visible for a range of networks, from having a sensitivity of 1 to the point where I_A is maximized. In this region, the two quantities are weakly correlated and attractors can be almost arbitrarily robust to perturbations without restricting the propagation of information in the network. ______________________________________ Dawn R. Gilpin, PhD Walter Cronkite School of Journalism & Mass Communication Arizona State University [log in to unmask] @drgilpin _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.