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Detecting network communities via greedy expanding based on local superiority index

Author

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  • Zhu, Junfang
  • Ren, Xuezao
  • Ma, Peijie
  • Gao, Kun
  • Wang, Bing-Hong
  • Zhou, Tao

Abstract

Community detection is a significant and challenging task in network science. Nowadays, plenty of attention has been paid on local methods for community detection. Greedy expanding is a popular and efficient class of local algorithms, which typically starts from some selected central nodes and expands those nodes to obtain provisional communities by optimizing a certain quality function. In this paper, we propose a novel index, called local superiority index (LSI), to identify central nodes. In the process of expansion, we use a fitness function to estimate the quality of provisional communities and ensure that all provisional communities must be weak communities. Evaluation based on the normalized mutual information suggests: (1) LSI is superior to the global maximal degree index and the local maximal degree index on most considered networks; (2) The proposed greedy algorithm based on LSI is better than some state-of-the-art algorithms on most considered networks.

Suggested Citation

  • Zhu, Junfang & Ren, Xuezao & Ma, Peijie & Gao, Kun & Wang, Bing-Hong & Zhou, Tao, 2022. "Detecting network communities via greedy expanding based on local superiority index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004782
    DOI: 10.1016/j.physa.2022.127722
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    References listed on IDEAS

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