Community Detection of Heterogeneous Networks with RankClus Based on Spark GraphX

Abstract

Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks without distinguishing different types of objects and links in the networks. Compared with the homogeneous networks, community detection based on the heterogeneous networks could obtain more accurate community structure. Most of contemporary community detection tasks only work on small dataset and fail to consider the quick and parallel process on big data. In this paper, we propose a method to detect communities in heterogeneous networks and solve the parallelisim problem on big networked data. Firstly, we improve and optimize the RankClus algorithm for community detection in heterogeneous networks. Then, we parallelize the improved algorithm based on spark graphx. Finally, we present the experiments of the parallel algorithm and compare the different experiment results which demenstrates that our method is applicable and has efficient results for community detection of heterogeneous networks.