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Social Group Query Based on multi-fuzzy-constrained strong simulation

ACM Transactions on Knowledge Discovery from Data 2021年第3期16卷 1–27页

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education and Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) and Hefei University of Technology Macquarie University NSW Australia Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education and Mininglamp Academy of Sciences Mininglamp Technology

Traditional social group analysis mostly uses interaction models, event models, or other social network analysis methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interactions between members of the group. Generally, Graph Pattern Matching (GPM) technique is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this article, first, a social group query problem that needs to consider the trust between members of the group is proposed. Then, to solve this problem, a multi-fuzzy-constrained strong simulation matching model is proposed based on multi-constrained simulation, and a strong simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence is proposed. Aiming at the inefficiency of the NTSS algorithm when pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matching edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm.

关键词： Graph pattern matching contextual social network multi-fuzzy-constrained strong simulation social group query

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