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Detecting Overlapping Community in Complex Network Based on Node Similarity

Zuo Chen^{1, 2}, Mengyuan Jia¹, Bing Yang³ and Xiaodong Li¹

1. College of Computer Science and Electronic Engineering, Hunan University
   Changsha, Hunan 410082, China
   chenzuo@iie.ac.cn
2. Institute of Information Engineering, Chinese Academy of Sciences
   Beijing 100093, China
3. School of Education, Hubei University
   Wuhan, Hubei 430062, China
   yangbing@126.com

Abstract

Overlapping communities in complex network is a common phenomenon in real world network. The overlapping community structure can more accurately obtain the actual structure information in the network. But at present the study of overlapping community division algorithm is relatively less, facing the problems of the low accurate rate. Based on this, this paper presents algorithms OCNS for detecting community overlapping base on node similarity. The algorithm calculates similarity between two nodes in the network by means of Jaccard similarity measure formula. Then the related nodes are adaptive merged according to the similarity value, combining with the community according to the change of modularity. The process of partitioning can not only accurately merge closely linked nodes in the network, but also find the overlapping nodes and bridge nodes between communities. The experiment proved the algorithm is effective to detect the overlapping community and has obvious advantages in the division of baseline social network Zachary and dolphin network, and the quality of division better than other existing partitioning algorithm.

Key words

complex network, community structure, node similarity, modularity

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS141021029C

Publication information

Volume 12, Issue 2 (June 2015)
Year of Publication: 2015
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Chen, Z., Jia, M., Yang, B., Li, X.: Detecting Overlapping Community in Complex Network Based on Node Similarity. Computer Science and Information Systems, Vol. 12, No. 2, 843–855. (2015), https://doi.org/10.2298/CSIS141021029C