JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2022, Vol. 57 ›› Issue (12): 1-12.doi: 10.6040/j.issn.1671-9352.7.2021.097

   

Community discovery algorithm based on label attention mechanism

WANG Jing-hong1,2, LIANG Li-na1, LI Hao-kang1, WANG Xi-zhao3*   

  1. 1. School of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, Hebei, China;
    2. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics &
    Data Security(Hebei Normal University), Shijiazhuang 050024, Hebei, China;
    3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518000, Guangdong, China
  • Published:2022-12-05

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Abstract: For network clustering, this paper proposes a community discovery algorithm based on label attention mechanism. Network features are jointly measured by label node frequency and inverse example node frequency, and attention mechanism is introduced to handle network features in order to make the metric of network features more focused on the detailed information of example nodes. Community division is composed of three parts: complex network preprocessing, network node strategy, and community game merging. The network nodes strategy is consists of three steps: the non-contributing node merging, the judgment of the node to the community and the judgment of the degree of the node. The experiments are verified with the help of real networks. The results show that the community discovery algorithm based on label attention mechanism outperforms other community discovery algorithms in four aspects: normalized mutual information, modularity, number of community divisions and running time. Applying this algorithm in real life can more intuitively show the existing connections within the network.

Key words: complex network, label node frequency, the inverse example node frequency, attention mechanism, community discovery

CLC Number: 

  • TP391
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