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International Conference on Data Mining and Big Data

DMBD 2022: Data Mining and Big Data pp 215–230Cite as

A Self-adaptive Two-Stage Local Expansion Algorithm for Community Detection on Complex Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1745))

Abstract

Community detection is of great importance to find hidden information in complex networks. For this problem, local expansion algorithms are becoming popular due to the low time complexity. However, most of them depend heavily on seed selection or require setting some thresholds in advance, leading to inaccurate partition. To this end, this paper proposes a self-adaptive two-stage local expansion algorithm (SALEA) for community detection. Specifically, we propose a self-adaptive strategy that can be used in SALEA for finding communities spontaneously. In the first stage, we apply the self-adaptive strategy for nodes to conduct local expansion and get coarse community structures. In the second stage, we apply the self-adaptive strategy for weak communities obtained in the first stage to refine the coarse community structures and get more accurate partitions. Finally, the experimental results on real and synthetic networks demonstrate that SALEA is superior over several state-of-the-arts.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61976001, 61876184), the Key Projects of University Excellent Talents Support Plan of Anhui Provincial Department of Education (gxyqZD2021089), and the Natural Science Foundation of Anhui Province (2008085QF309).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China

    Hui Shan, Bin Li, Haipeng Yang & Lei Zhang

Authors
  1. Hui Shan
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  2. Bin Li
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  3. Haipeng Yang
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  4. Lei Zhang
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Corresponding author

Correspondence to Lei Zhang .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Shan, H., Li, B., Yang, H., Zhang, L. (2022). A Self-adaptive Two-Stage Local Expansion Algorithm for Community Detection on Complex Networks. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_16

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  • DOI: https://doi.org/10.1007/978-981-19-8991-9_16

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  • Print ISBN: 978-981-19-8990-2

  • Online ISBN: 978-981-19-8991-9

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