ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (1): 70-82.doi: 10.7544/issn1000-1239.2021.20190775

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Target Community Detection with User Interest Preferences and Influence

Liu Haijiao1, Ma Huifang1,2, Zhao Qiqi1, Li Zhixin2   

  1. 1(College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070);2(Guangxi Key Laboratory of Multi-Source Information Mining and Security (Guangxi Normal University), Guilin, Guangxi 541004)
  • Online:2021-01-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61762078, 61363058,61966004), the Research Fund of Guangxi Key Laboratory of Multi-Source Information Mining & Security (MIMS18-08), and the Research Fund of Northwest Normal University Young Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2).

Abstract: Target community detection is to find the cohesive communities consistent with user’s preference. However, all the existing works either largely ignore the outer influence of the communities, or not “target-based”, i.e., they are not suitable for a target request. To solve the above problems, in this paper, the target community detection with user interest preferences and influence (TCPI) is proposed to locate the most influential and high-quality community related to user’s preference. Firstly, the node structure and attribute information are synthesized, and maximum k-cliques containing sample nodes are investigated as the core of the potential target community, and an entropy weighted attribute weight calculation method is designed to capture the attribute subspace weight of the potential target community. Secondly, the internal compactness and the external separability of the community is defined as the community quality function and the high-quality potential target community is expanded with each of the maximum k-cliques as the core. Finally, the external impact score of the community is defined, and all potential target communities are ranked according to the quality function and the external impact score of the community, and the communities with higher comprehensive quality are decided as the target communities. In addition, a pruning strategy of two-level is designed to improve the performance and efficiency of the algorithm after calculating the attribute subspace weights of all maximal k-cliques. Experimental results on synthetic networks and real-world network datasets verify the efficiency and effectiveness of the proposed method.

Key words: user interest preference, maximal k-clique, attribute subspace, community influence, target community detection

CLC Number: