Density Sensitive Random Walk for Local Community Detection

Given a network, local community detection aims at finding the community that contains a set of query nodes (seed nodes). Random walk (RW) based algorithms have shown great success in various local community detection scenarios. Starting from the seed nodes, RW based algorithms continuously sample random walk paths to get the clustering result. However, current RW based algorithms for local clustering are faced with the following two problems. The random walker is insensitive to the community boundary and might have an unbalanced walk. These problems would have a negative effect on the clustering result of RW based algorithms. In this paper, we propose a density sensitive random walk algorithms (DSRW) for local community detection. By integrating the graph density information into the random walk process, the problems are resolved and the clustering result is improved. We provide the convergence proof of DSRW algorithm and perform extensive experiments on both real and synthetic datasets. Results show that our DSRW algorithm has achieved the state-of-the-art result in most scenarios.