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Explainable community detection

Author

Listed:
  • Sun, Xiaoxuan
  • Hu, Lianyu
  • Liu, Xinying
  • Jiang, Mudi
  • Liu, Yan
  • He, Zengyou

Abstract

Community detection is a fundamental task in complex network analysis, aiming to partition networks into tightly multiple dense subgraphs. While community detection has been widely studied, existing methods often lack interpretability, making it challenging to explain key aspects such as node assignments, community boundaries, and inter-community relationships. In this paper, the explainable community detection issue is addressed, in which each community is characterized using a central node and its corresponding radius. The central node represents the most representative node in the community, while the radius defines its influence scope. Such an explainable community detection issue is formulated as an optimization problem in which the objective is to maximize central node’s coverage and accuracy in explaining its associated community. To solve this problem, two algorithms are developed: a naive algorithm and a fast approximate algorithm that incorporate heuristic strategies to improve computational efficiency. Experimental results on 9 real-world networks demonstrate that the proposed methods can effectively interpret community structures with high accuracy and efficiency. More precisely, the objective function values achieved by the identified pairs of center and radius exceed 0.7 on most communities and the running time is generally no more than 10 s on a network with approximatively one thousand nodes. The source code of the proposed methods can be found at: https://github.com/xuannnn523/CCTS.

Suggested Citation

  • Sun, Xiaoxuan & Hu, Lianyu & Liu, Xinying & Jiang, Mudi & Liu, Yan & He, Zengyou, 2025. "Explainable community detection," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002115
    DOI: 10.1016/j.chaos.2025.116198
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    References listed on IDEAS

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    1. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    2. Gupta, Naveen & Singh, Anurag & Cherifi, Hocine, 2016. "Centrality measures for networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 46-59.
    3. Tianxi Li & Lihua Lei & Sharmodeep Bhattacharyya & Koen Van den Berge & Purnamrita Sarkar & Peter J. Bickel & Elizaveta Levina, 2022. "Hierarchical Community Detection by Recursive Partitioning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 951-968, April.
    4. Yuanzhi Yang & Lei Yu & Xing Wang & Siyi Chen & You Chen & Yipeng Zhou, 2020. "A novel method to identify influential nodes in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-14, February.
    5. Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
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