ITC: Influential-Truss Community Search

Abstract

Community search is a method of finding a com-munity closely related to a query node. The latest influence community search considers both the structural cohesion of the community and the influence between nodes. It sets the influence threshold to constrain the output community. However, artificially setting the influence threshold makes the output community too large or too small, which leads to low accuracy of the output community. In order to avoid the low accuracy of community search caused by artificially setting influence thresh-old constraints, this paper studies the community search problem based on community influence score. In this paper, an influence-truss community (ITC) model is proposed for community search by combining structural cohesion and community influence score. This model aims to obtain a connected subgraph in a social network containing the query node, which satisfies structural cohesion and satisfies the subgraph's maximum community in-fluence score. In order to obtain ITC, an effective pruning method is proposed, which strips other nodes far away from the query node. Then, the ITCS algorithm is designed, which firstly imposes structural cohesion constraints on query nodes. Then, the search community's influence scores are iteratively calculated until the community has the highest community influence score under the condition of meeting the structural cohesion. Experiments on real-world networks of different scales show that the community search accuracy index of ITCS is improved by about 20% compared with the traditional method.