Joint Nonnegative Matrix Factorization for Community Structures Detection in Signed Networks

TL;DR

This paper introduces a joint nonnegative matrix factorization method for detecting community structures in signed networks, providing a new approach to understand network topology and functions like information diffusion.

Contribution

It proposes a novel joint nonnegative matrix factorization model and a modified partition density for community detection and evaluation in signed networks.

Findings

Effective on synthetic networks

Validated on real-world networks

Accurately determines number of communities

Abstract

Community structures detection in signed network is very important for understanding not only the topology structures of signed networks, but also the functions of them, such as information diffusion, epidemic spreading, etc. In this paper, we develop a joint nonnegative matrix factorization model to detect community structures. In addition, we propose modified partition density to evaluate the quality of community structures. We use it to determine the appropriate number of communities. The effectiveness of our approach is demonstrated based on both synthetic and real-world networks.