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Efficient multi-scale community search method based on spectral graph wavelet

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Abstract

Community search is an important problem in network analysis, which has attracted much attention in recent years. As a query-oriented variant of community detection problem, community search starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. The existing community search method typically returns a single target community containing query nodes by default. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, query nodes are expected to be located in multiple communities with different semantics. To address this limitation of existing methods, an efficient spectral-based Multi-Scale Community Search method (MSCS) is proposed, which can simultaneously identify the multi-scale target local communities to which query node belong. In MSCS, each node is equipped with a graph Fourier multiplier operator. The access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community scales. In addition, an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral methods. Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

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Acknowledgements

This work was supported by the Industrial Support Project of Gansu Colleges (2022CYZC-11), National Natural Science Foundation of China (61363058, 61966004), Northwest Normal University Young Teachers Research Capacity Promotion Play (NWNU-LKQN2019-2) and Natural Science Foundation of Gansu Province (21JR7RA114).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Cairui Yan, Huifang Ma, Qingqing Li & Fanyi Yang

  2. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Huifang Ma & Zhixin Li

Authors
  1. Cairui Yan
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  2. Huifang Ma
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  3. Qingqing Li
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  4. Fanyi Yang
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  5. Zhixin Li
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Corresponding author

Correspondence to Huifang Ma.

Additional information

Cairui Yan is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. Her general area of research is multi-scale community mining.

Huifang Ma is currently a professor in the College of Computer Science and Engineering at Northwest Normal University, China. Her general area of research is data mining and machine learning.

Qingqing Li is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. Her general area of research is local community detection.

Fanyi Yang is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. His general area of research is polarized communities discovering.

Zhixin Li is currently a professor in the College of Computer Science and Information Engineering at Guangxi Normal University, China. His general area of research is natural language processing and machine learning.

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Yan, C., Ma, H., Li, Q. et al. Efficient multi-scale community search method based on spectral graph wavelet. Front. Comput. Sci. 17, 175335 (2023). https://doi.org/10.1007/s11704-022-2220-4

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  • DOI: https://doi.org/10.1007/s11704-022-2220-4

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