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Overlapping Community Detection with Two-Level Expansion by Local Clustering Coefficients

  • Yi-Jen Su
  • Che-Chun Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)

Abstract

Community detection is crucial to Social Network Analysis (SNA) in that it helps to discover high-density overlapping communities hidden in complex networks for advanced applications. This study proposed a novel community detection method by seed set expansion. The method gathered meaningful nodes into a seed set, which was then used as a central node to merge neighbor nodes until communities were found. To enhance efficiency, a two-level expansion approach was further developed, which adopted the 80/20 rule and involved threshold change in order to discover cohesive subgroups of smaller sizes. To detect overlapping communities, local clustering coefficients (LCC) were calculated to measure the interaction density between neighbor nodes and determine whether they expanded or not. The experiment results were evaluated by measuring the cohesion quality of communities.

Keywords

Social network analysis Community detection Clustering coefficients 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shu-Te UniversityKaohsiung CityTaiwan

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