Overlapping Community Detection in Complex Networks using Symmetric Binary Matrix Factorization

TL;DR

This paper introduces a symmetric binary matrix factorization model for detecting overlapping communities in networks, enabling explicit community assignment and outlier detection, validated on synthetic and real-world data.

Contribution

The paper presents a novel SBMF model that explicitly identifies overlapping communities and outliers, along with a modified partition density for quality assessment.

Findings

Effective detection of overlapping communities demonstrated on benchmarks

Outlier nodes can be distinguished from overlapping nodes

Model determines optimal number of communities using modified partition density

Abstract

Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model (SBMF) to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a modified partition density to evaluate the quality of community structures. We use this to determine the most appropriate number of communities. We evaluate our methods using both synthetic benchmarks and real world networks, demonstrating the effectiveness of our approach.