Discovering Communities in Heterogeneous Social Networks Based on Non-negative Tensor Factorization and Cluster Ensemble Approach.

Identification of the appropriate community structure in social networks is an arduous task. The intricacy of the problem increases with the heterogeneity of multiple types of objects and relationships involved in the analysis of the network. Traditional approaches for community detection focus on the networks comprising of content features and linkage information of the set of single type of entities. However, rich social media networks are usually heterogeneous in nature with multiple types of relationships existing between different types of entities. Cognizant to these requirements, we develop a model for community detection in Heterogeneous Social Networks HSNs employing non-negative tensor factorization method and cluster ensemble approach. Extensive experiments are performed on 20Newsgroup dataset which establish the effectiveness and efficiency of our scheme.