Community Detection in Multi-Layer Graphs

Abstract

Community detection, also known as graph clustering, has been extensively studied in the literature. The goal of community detection is to partition vertices in a complex graph into densely-connected components socalled communities. In recent applications, however, an entity is associated with multiple aspects of relationships, which brings new challenges in community detection. The multiple aspects of interactions can be modeled as a multi-layer graph comprised of multiple interdependent graphs, where each graph represents an aspect of the interactions. Great efforts have therefore been made to tackle the problem of community detection in multi-layer graphs. In this survey, we provide readers with a comprehensive understanding of community detection in multi-layer graphs and compare the state-of-the-art algorithms with respect to their underlying properties.