ISOCD: Overlapping community detection algorithm based on local influence and similarity

Jinfang Sheng, Wenzhe Jiao, Bin Wang, Shuang Liu, Yingxian Liu and Qiang Zhong
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Jinfang Sheng: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Wenzhe Jiao: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Bin Wang: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Shuang Liu: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Yingxian Liu: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Qiang Zhong: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 09, 1-21

Abstract: Many nodes of a complex network in the real world belong to multiple communities. Research on the detection of overlapping communities with common nodes can help us understand the topology of network and extract information hidden in the network. Considering the problems of many existing overlapping community detection methods, such as high time complexity or poor division results, we propose an overlapping community detection method based on local influence and similarity (ISOCD). The ISOCD method selects seed nodes based on local influence, follows a greedy strategy, takes the fitness function of similarity as the optimization goal, and finally optimizes and merges the detected communities. In experiments, the method we propose has good community division effects both on the real-world and synthetic networks, and has low time complexity.

Keywords: Complex network; community detection; local influence; similarity degree (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123501139

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