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Local community detection algorithm based on local modularity density

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Abstract

Compared to global community detection, local community detection aims to find communities that contain a given node. Therefore, it can be regarded as a specific and personalized community detection task. Local community detection algorithms based on modularity are widely studied and applied because of their concise strategies and prominent effects. However, they also face challenges, such as sensitivity to seed node selection and unstable communities. In this paper, a local community detection algorithm based on local modularity density is proposed. The algorithm divides the formation process of local communities into a core area detection stage and a local community extension stage according to community tightness based on the Jaccard coefficient. In the core area detection stage, the modularity density is used to ensure the quality of the communities. In the local community extension stage, the influence of nodes and the similarity between the nodes and the local community are utilized to determine boundary nodes to reduce the sensitivity to seed node selection. Experimental results on real and artificial networks demonstrated that the proposed algorithm can detect local communities with high accuracy and stability.

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  1. https://github.com/lcx945/localcommunity

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Acknowledgements

This work is partly supported by the National Natural Science Foundation of China under Grant No. 61672159, No. 61672158, No. 62002063 and No. 61300104, the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No. 2017H6008 and No. 2018H6010, the Natural Science Foundation of Fujian Province under Grant No. 2018J07005, No. 2019J01835, No. 2020J05112 and No. 2020J01494, and Haixi Government Big Data Application Cooperative Innovation Center.

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Authors and Affiliations

  1. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou, 350116, China

    Kun Guo, Xintong Huang, Ling Wu & Yuzhong Chen

  2. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350116, China

    Kun Guo & Yuzhong Chen

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  1. Kun Guo
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  2. Xintong Huang
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  3. Ling Wu
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  4. Yuzhong Chen
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Correspondence to Yuzhong Chen.

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Guo, K., Huang, X., Wu, L. et al. Local community detection algorithm based on local modularity density. Appl Intell 52, 1238–1253 (2022). https://doi.org/10.1007/s10489-020-02052-0

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