Seed-Oriented Local Community Detection Based on Influence Spreading

Abstract

In recent years, local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks. However, there are still some issues that need to be further studied. First, there is no local community detection algorithm dedicated to detecting a seed-oriented local community, that is, the local community with the seed as the core. The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters, respectively. To solve the existing problems, we propose a seed-oriented local community detection algorithm, named SOLCD, that is based on influence spreading. First, we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes. Second, we obtain the seed-oriented local community, which is composed of the may-members and the must-member chain of the seed, by detecting the influence scope of the seed. The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed. Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks. The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters. In addition, when taking nodes with different influence as seeds, SOLCD can stably obtain high-quality seed-oriented local communities.