Existing community search methods are used to explore communities in undirected graphs that meet cohesion and influence requirements,without considering the impact of edge direction in directed graphs.This oversight leads to insufficient influence and cohesion in the results of community detection on directed graphs.The problem of influence community search on directed graphs was proposed,and a corresponding online search algorithm was designed.To fur-ther enhance the efficiency of community mining,an index-based influence search method on directed graphs and its op-timization strategies were proposed.In addition,a parallel-based index construction method was proposed to accelerate the index building process.Finally,based on eight real-world datasets,validation is conducted,and the experimental re-sults confirm the effectiveness and efficiency of the proposed algorithm.
关键词
社区搜索/有向图/Truss模型/影响力
Key words
community search/directed graph/Truss model/influence
维普
万方数据