Abstract
In this paper we present a framework to study evolution of communities in dynamic networks. A dynamic network is represented by a sequence of static graphs named as network snapshots.We introduce a distance measure between static graphs to study similarity among network snapshots and to detect outlier events. To find a detailed structure within each network snapshot we used a modularity maximization algorithm based on a fast greedy search extended with a random walk approach. Community detection often results in a different number of communities in different network snapshots. To make communities evolution studies feasible we propose a greedy method to match clustering labels assigned to different networks. The suggested framework is applied for analysis of dynamic networks built from real-world mobile datasets.
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Nefedov, N. (2013). Analysis of Communities Evolution in Dynamic Social Networks. In: Ghoshal, G., Poncela-Casasnovas, J., Tolksdorf, R. (eds) Complex Networks IV. Studies in Computational Intelligence, vol 476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36844-8_4
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DOI: https://doi.org/10.1007/978-3-642-36844-8_4
Publisher Name: Springer, Berlin, Heidelberg
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