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A Local Greedy Search Method for Detecting Community Structure in Weighted Social Networks

  • Bin Liu
  • Tieyun Qian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

In this paper, we give a new definition of community which is composed of two parts: community core and the periphery. Community core consists of highly densely connected nodes. And we propose LGSM (Local Greedy Search Method) for discovering community structures in social networks. LGSM sorts node according to weighted degree. For each node, LGSM derives a maximal weighted clique as a seed cluster. Then, LGSM adds new nodes into the seed cluster until the weighted edge density is smaller than the threshold value. After all community cores are detected, LGSM allots isolated nodes to the detected cores, and optimizes the community structure based on modularity. Our method is an integrative method, which is applicable not only to discovering overlapping communities, but also to discovering non-overlapping community. Experiments illustrate that LGSM can achieve good community structure on synthetic and real-world networks and the time complexity is O(|E|lg(|V|)).

Keywords

overlapping community core community structure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bin Liu
    • 1
  • Tieyun Qian
    • 2
  1. 1.Computer SchoolWuhan UniversityWuhanChina
  2. 2.State Key Lab of Software EngineeringWuhan UniversityWuhanChina

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