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Community discovery algorithm of complex network attention model

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Abstract

In terms of understanding the structure of complex networks or the functional characteristics of complex networks, community discovery is of great significance. This paper uses the attention model to combine the second-order neighbor similarity matrix with the modularity matrix, extracts relatively more comprehensive complex network feature information from multiple angles for network division. Firstly, perform complex network preprocessing, and perform division preprocessing according to the value of the attention similarity matrix. Secondly, complete the merger of the community game according to the connection strength between the two different communities. By comparing with other algorithms on computer-generated networks and real-world networks, it is proved that this algorithm has obtained good results in terms of the number of communities, running time, normalized mutual information and modularity.

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Acknowledgements

This work has been supported by the Natural Science Foundation of Hebei Province (F2019205303), the project funded by The Introduction of Overseas Students in Hebei Province (C20200340), the Hebei Normal University Science and Technology Fund Project (L2019Z10).

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Authors and Affiliations

  1. Hebei Normal University College of Computer and Cyber Security, Shijiazhuang, 050024, Hebei, China

    Jinghong Wang, Haokang Li & Lina Liang

  2. University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Jinghong Wang

  3. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security, Hebei Normal University, Shijiazhuang, 050024, Hebei, China

    Jinghong Wang, Haokang Li & Lina Liang

  4. Science and Technology Division, Shanghai Customs District P R China, Shanghai, 200002, China

    Yi Zhou

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  1. Jinghong Wang
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  2. Haokang Li
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  3. Lina Liang
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  4. Yi Zhou
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Correspondence to Jinghong Wang or Yi Zhou.

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Wang, J., Li, H., Liang, L. et al. Community discovery algorithm of complex network attention model. Int. J. Mach. Learn. & Cyber. 13, 1619–1631 (2022). https://doi.org/10.1007/s13042-021-01471-w

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  • DOI: https://doi.org/10.1007/s13042-021-01471-w

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