Index Based Efficient Algorithms For Closest Community Search

The community search problem is defined as finding densely connected subgraphs in a large graph containing a given set of query nodes. One of the limitations of many current community search models is that detected communities may include irrelevant nodes, called the “free riders”. In this paper, we study the community search problem in the truss-based closest community model aimed to discover community for a given query set with avoiding the free rider effect. The Closest Truss Community (CTC) is a densely connected k-truss subgraph that contains the query nodes and has the smallest diameter. We use a greedy approach and propose a Truss EQuivalence based index graph (TEQ) that supports the search of closest trust community. To further improve the efficiency of the search, we create a maximum spanning tree of TEQ, as an index tree. We also propose an early pruning algorithm to improve the efficiency of free rider elimination. Extensive experiments on large real-world networks validate the efficiency and effectiveness of our algorithms over the state-of-the-art methods.