Extracting Community Structure in Multi-relational Network via DeepWalk and Consensus Clustering

Abstract

In the real world, entities are often connected via multiple relations, forming multi-relational network. These complex networks need novel models for their representation and sophisticated tools for their analysis. Community detection is one of the primary tools for the structural and functional analysis of the networks at the macroscopic level. Already a lot of research work has been done on discovering communities in the networks with only single relation. However, the research work on discovering communities in multi-relational network (MRN) is still in its early stages. In this article, we have proposed a novel approach to extract the communities in a multi-relational network using DeepWalk network embedding technique and Consensus clustering. Empirical study is conducted on the real-world publicly available Twitter datasets. In our observations we found that our proposed model performs significantly better than some of the baseline approaches based on spectral clustering algorithm, modularity maximization, block clustering and non-negative matrix factorization.