4.5 Article

Local community detection algorithm based on local modularity density

Journal

APPLIED INTELLIGENCE
Volume 52, Issue 2, Pages 1238-1253

Publisher

SPRINGER
DOI: 10.1007/s10489-020-02052-0

Keywords

Complex network; Local community detection; Local modularity density; Community extension

Funding

  1. National Natural Science Foundation of China [61672159, 61672158, 62002063, 61300104]
  2. Fujian Collaborative Innovation Center for Big Data Applications in Governments
  3. Fujian Industry-Academy Cooperation Project [2017H6008, 2018H6010]
  4. Natural Science Foundation of Fujian Province [2018J07005, 2019J01835, 2020J05112, 2020J01494]
  5. Haixi Government Big Data Application Cooperative Innovation Center

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This paper proposes a local community detection algorithm based on local modularity density, which incorporates new considerations in both the core area detection and community extension stages to improve robustness to seed node selection. Experimental results demonstrate that the algorithm can accurately and stably detect local communities.
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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