Computer Science ›› 2021, Vol. 48 ›› Issue (9): 244-250.doi: 10.11896/jsjkx.201100010

• Artificial Intelligence • Previous Articles     Next Articles

Overlapping Community Detection Algorithm Based on Subgraph Structure

CHEN Xiang-tao, ZHAO Mei-jie, YANG Mei   

  1. College of Computer Science and Electronic Engineering,Hunan University,Changsha 410000,China
  • Received:2020-11-02 Revised:2021-03-22 Online:2021-09-15 Published:2021-09-10
  • About author:CHEN Xiang-tao,born in 1973,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include data mining,heterogenous information network,social network and network embedding.
  • Supported by:
    National Natural Science Foundation of China(61873089),National Key R&D Program of China(2018YFC0910405) and National Natural Science Foundation of China(61572180)

Abstract: Local community detection algorithms usually select seed nodes for community detection.To improve the quality of effectiveness of seed node selection,we propose an overlapping community detection algorithm based on subgraph structure(SUSBOCD).This algorithm proposes a new measure of node importance,which not only considers the number of neighbors,but also considers the degree of density between neighbors.First,SUSBOCD selects the most important node that is not visited and the most similar neighbor node,and merges the two nodes and their common neighbor nodes to form an initial seed subgraph.The process runs iteratively until all nodes have been visited.Second,the similarity is judged according to the neighborhood information of the seed subgraph.If it is similar,it is merged to form the initial community structure.The process runs iteratively until all seed subgraphs are visited.Finally,we optimize the community.If there are nodes without assigned communities,they are added to the most similar community,and then the community structure with high overlap is merged.Experiments on real and artificial networks show that SUSBOCD can improve the quality of overlapping community partition effectively in the three evaluation indexes of ONMI,EQ and Omega.

Key words: Community expansion, Community optimization, Local expansion, Overlapping community detection, Seed selection

CLC Number: 

  • TP391
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