Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks. Recently, there needs to be more focus on the community search issue within directed graphs, even though substantial research has been carried out on undirected graphs. The recently proposed D-truss model has achieved good results in the quality of retrieved communities. However, existing D-truss-based work cannot perform efficient community searches on large graphs because it consumes too many computing resources to retrieve the maximal D-truss. To overcome this issue, we introduce an innovative merge relation known as D-truss-connected to capture the inherent density and cohesiveness of edges within D-truss. This relation allows us to partition all the edges in the original graph into a series of D-truss-connected classes. Then, we construct a concise and compact index, ConDTruss, based on D-truss-connected. Using ConDTruss, the efficiency of maximum D-truss retrieval will be greatly improved, making it a theoretically optimal approach. Experimental evaluations conducted on large directed graph certificate the effectiveness of our proposed method.

翻译：社区搜索是社区检测的一种衍生形式，能够实现在线且个性化的社区发现，在现实世界的大规模网络中有着广泛应用。尽管在无向图上已有大量研究，但近年来针对有向图上的社区搜索问题仍需更多关注。最新提出的D-truss模型在检索社区质量方面取得了良好效果。然而，现有基于D-truss的工作无法在大规模图上高效执行社区搜索，因为检索最大D-truss需要消耗过多计算资源。为克服这一问题，我们引入了一种称为D-truss连接的新型合并关系，以捕捉D-truss内部边所固有的密度与凝聚性。该关系使我们能将原始图中的所有边划分为一系列D-truss连通类。随后，我们基于D-truss连接构建了一种简洁紧凑的索引结构ConDTruss。利用ConDTruss，最大D-truss的检索效率将大幅提升，理论上达到最优性能。在大规模有向图上进行的实验评估验证了所提方法的有效性。