Social Group Query Based on Multi-Fuzzy-Constrained Strong Simulation

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