Network entropy based overlapping community detection in social networks

The structural analysis of Social networks is gaining more importance over recent years. The most important structural property of social network is community structure and to detect such structures a novel approach is proposed by adopting the information theoretic definition of Entropy to Networks i.e Network Entropy. It is observed that the quality of the community is decreased with higher values of network entropy. Hence, the proposed approach Entropy based Overlapping community detection (EOCD) finds communities that are having low network entropy. EOCD is tested on both real-world and synthetic datasets and the results are evaluated with three popular metrics F-score, Extended NMI and Overlap modularity. It produces high quality communities even for larger networks and delivers good performance over its baseline algorithms.

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