Netinfo Security ›› 2025, Vol. 25 ›› Issue (12): 1936-1947.doi: 10.3969/j.issn.1671-1122.2025.12.009

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Research on Community Detection and Core Node Discovery Based on Improved Louvain Algorithm

LIU Dahe1,2, XIU Jiapeng1,2(), YANG Zhengqiu1,2   

  1. 1. School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2. National Demonstration Software College, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2025-01-27 Online:2025-12-10 Published:2026-01-06
  • Contact: XIU Jiapeng E-mail:xiujiapeng@bupt.edu.cn

Abstract:

In the context of community detection, this paper proposed a community detection method based on the improved Louvain algorithm, and conducted in-depth mining of core nodes in the community by constructing a comprehensive scoring model combined with multiple centrality indicators. The improved algorithm significantly improved the accuracy and efficiency of community detection by merging redundant edges and optimizing node division. In the weight selection of the comprehensive scoring model, the particle swarm algorithm was introduced to reduce the search complexity, thereby further improving the model’s performance in core node identification. In the experiment, this paper used multiple data set networks to verify the effectiveness of this method. The results show that the improved Louvain algorithm has better community structure identification capabilities in complex networks. At the same time, by comparing the core node identification effects of different centrality indicators, it was found that the comprehensive scoring model combined with the particle swarm optimization algorithm has obvious advantages in information dissemination and node importance assessment. This research result provides an effective technical means for community detection and core node identification, and has the potential to be further expanded to larger-scale networks.

Key words: community detection, core node, Louvain, particle swarm

CLC Number: