Konstantin Mertsalov

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—Increasingly, methods to identify community structure in networks have been proposed which allow groups to overlap. These methods have taken a variety of forms, resulting in a lack of consensus as to what characteristics overlapping communities should have. Furthermore, overlapping community detection algorithms have been justified using intuitive(More)
We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communications (blogger-to-blogger links) in such online communication networks are very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two(More)
—Social networks that arise spontaneously and evolve over time have become an important component of ever growing global societies used for spreading ideas and indoctrinating people. Their loose membership and dynamics make them difficult to observe and monitor. We present a set of tools for discovery, analysis and monitoring evolution of hidden social(More)
—We study information diffusion in real-life and synthetic dynamic networks, using well known threshold and cascade models of diffusion. Our test-bed is the communication network of the LiveJournal Blogosphere. We observe that the dynamic and static versions of the Blogograph, yield very different behaviors of the diffusion. It was earlier discovered that(More)
Identifying communities is essential for understanding the dynamics of a social network. The prevailing approach to the problem of community discovery is to partition the network into disjoint groups of members that exhibit a high degree of internal communication. This approach ignores the possibility that an individual may belong to two or more groups.(More)
The primary focus of this paper is to describe stable statistics of the blogosphere's evolution which convey information on the social network's dynamics. In this paper, we present a number of non-trivial statistics that are surprisingly stable and thus can be used as benchmarks to diagnose phase-transitions in the network. We believe that stable statistics(More)
—We present a model of evolution of large social networks. Our model is based on the local nature of communication: a node's communication energy is spend mostly within it's small social area. We test our model on the Blog network hosted by LiveJournal. Our testing with different definitions of local areas shows that the best approximation to the observed(More)
3 Abstract—We present an extended version of a software system SIGHTS 1 (Statistical Identification of Groups Hidden in Time and Space), which can be used for the discovery, analysis, and knowledge visualization of social coalitions in communication networks such as Blog-networks. The evolution of social groups reflects information flow and social dynamics(More)