A New Algorithm for Detecting Local Community Based on Random Walk

Abstract

This paper presents one new algorithm for local community discovery. It employs a new vertex selection strategy which considers not only the boundary structure of candidate local community but also the probability which the investigated vertex will return to the candidate local community. A local random walk is adopted to compute this return probability which does not require the global information. We choose four algorithms for comparison which are the best ones existed by far. For better evaluation, the datasets include not only the computer generated graphs in standard benchmark but also the real-world networks which are classical ones in global community discovery. The experimental results show our algorithm outperforms the other ones on the computer generated graphs. The performance of our algorithm is approximately the same with the algorithm proposed by Luo, Wang and Promislow on real-world networks.