Digging into it: Community detection via hidden attributes analysis

Abstract

Identifying community structure in complex systems is essential for characterizing and understanding their functions and properties. Over the past decades, considerable efforts have been devoted to analyzing the community structure of networks and numerous community detection methods have consequently been developed. Among the proposed methods, none of them has explored the community membership in depth, which may provide useful information about the nodes and the communities. In this paper, we name the information contained in the community membership as hidden attributes of nodes and communities, and design a delicate nonnegative matrix factorization (a widely used framework for both disjoint and overlapping community detection) based model to extract the hidden attributes and use these hidden attributes to modify the community detection results on unannotated networks. To test our model’s expansibility, we also extend it on annotated networks by adding observed nodes’ attributes into it. Experiment results on both unannotated and annotated real-world networks show superior performance of our model over state-of-the-art approaches.