Relative Validity Criteria for Community Mining Algorithms
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Social influence based clustering of heterogeneous information networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Complex systems have been widely studied to characterize their structural behaviors from a topological perspective. High modularity is one of the recurrent features of real-world complex systems. Various graph clustering algorithms have been applied to identifying communities in social networks or modules in biological networks. However, their applicability to real-world systems has been limited because of the massive scale and complex connectivity of the networks. In this study, we exploit a novel information-theoretic model for graph clustering. The entropy-based clustering approach finds locally optimal clusters by growing a random seed in a manner that minimizes graph entropy. We design and analyze modifications that further improve its performance. Assigning priority in seed-selection and seed-growth is well applicable to the scale-free networks characterized by the hub-oriented structure. Computing seed-growth in parallel streams also decomposes an extremely large network efficiently. The experimental results with real biological and social networks show that the entropy-based approach has better performance than competing methods in terms of accuracy and efficiency.