Influence community search on directed graphs - DU Ming; HU Xinyu; ZHOU Junfeng (Tongxin xuebao)
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 further enhance the efficiency of community mining, an index-based influence search method on directed graphs and its optimization 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 results confirm the effectiveness and efficiency of the proposed algorithm.