Density Sensitive Random Walk for Local Community Detection

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.

Local Community Detection: A Survey

“…Then, this probability distribution is considered as a measure of the similarity between the starting node and its neighbors. Nodes with higher similarity values indicate that they are more likely to be placed in the seed community [132]. Most algorithms in this category can be classified into three main groups: 1) PageRank, 2) Heat-kernel and 3) Local spectral based.…”

Universal Multilayer Network Exploration by Random Walk with Restart

“…MultiXrank output scores can be used in a wide variety of applications. Indeed, RWR scores can be employed directly for node prioritization, and they can also be the starting point for clustering (22)(23)(24) or embedding (25)(26)(27), for instance. We illustrate here the versatility and usefulness of MultiXrank output scores in different use-cases.…”