Abstract
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.
Highlights
Network is a common representation structure for many real-world relations
For the existing random walk based algorithms for local community detection, i.e., random walk with restart (RWR) [9], vertex reinforcement random walk (VRW) [7], Color random walk algorithm (CRW) [20], etc., none of these algorithms can determine whether the random walker agent is at the community boundary position
Results show that density sensitive random walk (DSRW) algorithm achieves higher performance compared to the state-of-the-art methods
Summary
Network is a common representation structure for many real-world relations (e.g. the social network). Random walk (RW) [7], [9], [10], [17], [20], [22] based algorithms are the most prevalent methods used in local community detection In these methods, random walk paths are repeatedly sampled around the seed nodes until a converged random walker visiting probability distribution for nodes is obtained. For the existing random walk based algorithms for local community detection, i.e., RWR [9], VRW [7], CRW [20], etc., none of these algorithms can determine whether the random walker agent is at the community boundary position. The network is colored by the converged visiting probability of the random walker in each algorithm It shows that DSRW algorithm has a more balanced walk inside the community and fewer visits for the nodes outside the community. All the datasets and codes used in this paper will be open source later
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