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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
Dynamics in online social networks
Przemyslaw A. Grabowicz, Jose J. Ramasco, Victor M. Eguiluz
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
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
Control Centrality and Hierarchical Structure in Complex Networks
Liu Y-Y, Slotine J-J, Barabási A-L (2012)
PLoS ONE 7(9): e44459.
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.
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
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