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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 
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. 

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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