Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6): 128-134.doi: 10.11896/jsjkx.231000142

• Database & Big Data & Data Science • Previous Articles     Next Articles

Motif-aware Adaptive Cross-layer Random Walk Community Detection

WANG Beibei1, XIN Junchang2, CHEN Jinyi1, WANG Zhiqiong3   

  1. 1 School of Computer Science and Engineering,Northeastern University,Shenyang 110819,China
    2 Key Laboratory of Big Data Management and Analytics(Liaoning Province),Shenyang 110819,China
    3 College of Medicine and Biological Information Engineering,Northeastern University,Shenyang 110819,China
  • Received:2023-10-20 Revised:2024-04-02 Online:2024-06-15 Published:2024-06-05
  • About author:WANG Beibei,born in 1998,postgra-duate.Her main research interests include social network analysis and so on.
    XIN Junchang,born in 1977,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.48169S).His main research interests include blockchain technology,medical informatics and big data management and analytics.
  • Supported by:
    National Key Research and Development Program of China(2021YFB3300900),National Natural Science Foundation of China(62072089) and Fundamental Research Funds for the Central Universities of Ministry of Education of China(N2116016).

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Abstract: In recent years,multi-layer network community detection using high order interactive information has become a hot spot.In order to solve this problem,a MACLCD algorithm is proposed.The algorithm considers high order interaction and interlayer correlation in multi-layer network to improve the accuracy of community detection.Specifically,firstly,the inter-layer correlation is revealed through comprehensive measurement from the perspective of network and node.Secondly,considering that each layer network may have different local and global structural characteristics,motif is used to identify the unique high-order interaction structure of each layer network,and a multi-layer weighted hybrid order network is constructed.Furthermore,a cross-layer walking model is designed,and a jump factor is introduced to ensure that the random walk can traverse the multi-layer network adaptively,so as to capture more diverse network structural information.Experimental comparisons are conducted on four real-world network datasets,and the results demonstrate that the MACLCD algorithm outperforms the comparison algorithms in terms of community detection performance.

Key words: Community detection, Multi-layer networks, High-order structure, Cross-layer random walk, motif

CLC Number: 

  • TP391
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