Detecting network communities via greedy expanding based on local superiority index

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

Physica A: Statistical Mechanics and its Applications, 2022, vol. 603, issue C

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.

Keywords: Complex networks; Community detection; Local superiority index; Greedy algorithm (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004782

DOI: 10.1016/j.physa.2022.127722

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