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NSLS: A Neighbor Similarity and Label Selection-Based Algorithm for Community Detection

Author

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  • Shihu Liu

    (School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China
    Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University, Quanzhou 362000, China)

  • Hui Chen

    (School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China)

  • Shuang Li

    (School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China)

  • Xiyang Yang

    (Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University, Quanzhou 362000, China
    School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China)

Abstract

Community detection is still regarded as one of the most applicable methods for discovering latent information in complex networks. Recently, many similarity-based community detection algorithms have been widely applied to the analysis of complex networks. However, these approaches may also have some limitations, such as relying solely on simple similarity measures, which makes it difficult to differentiate the tightness of the relation between nodes. Aiming at this issue, this paper proposes a community detection algorithm based on neighbor similarity and label selection (NSLS). Initially, the algorithm assigns labels to each node using a new local similarity measure, thereby quickly forming a preliminary community structure. Subsequently, a similarity parameter is introduced to calculate the similarity between nodes and communities, and the nodes are reassigned to more appropriate communities. Finally, dense communities are obtained by a fast-merge method. Experiments on real-world networks show that the proposed method is accurate, compared with recent and classical community detection algorithms.

Suggested Citation

  • Shihu Liu & Hui Chen & Shuang Li & Xiyang Yang, 2025. "NSLS: A Neighbor Similarity and Label Selection-Based Algorithm for Community Detection," Mathematics, MDPI, vol. 13(8), pages 1-24, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1300-:d:1635477
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    References listed on IDEAS

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    1. Laassem, Brahim & Idarrou, Ali & Boujlaleb, Loubna & Iggane, M’bark, 2022. "Label propagation algorithm for community detection based on Coulomb’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Swarup Chattopadhyay & Tanmay Basu & Asit K. Das & Kuntal Ghosh & Late C. A. Murthy, 2021. "Towards effective discovery of natural communities in complex networks and implications in e-commerce," Electronic Commerce Research, Springer, vol. 21(4), pages 917-954, December.
    3. Li, Huxiong & Nasab, Samaneh Salehi & Roghani, Hamid & Roghani, Parya & Gheisari, Mehdi & Fernández-Campusano, Christian & Abbasi, Aaqif Afzaal & Wu, Zongda, 2024. "LMFLS: A new fast local multi-factor node scoring and label selection-based algorithm for community detection," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    4. Saoud, Bilal & Moussaoui, Abdelouahab, 2018. "Node similarity and modularity for finding communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1958-1966.
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