Survey on Community Detection in Multi-layer Networks

Abstract

Community detection is one of the most important tasks of complex network analysis. Most of the existing community detection methods are oriented to single-layer networks, and the research on community detection in multi-layer networks widely existing in the real world is slightly insufficient. This paper first presents the definition of various multi-layer networks, compares the characteristics of each network in terms of node alignment, inter-layer edges and inter-layer coupling, and then introduces various traditional single-layer network community discovery methods. On this basis, this paper deeply surveys the multi-layer network community detection methods, which are roughly divided into aggregation-based methods and extension-based methods. The methods are analyzed and compared in terms of mechanisms, advantages, limitations, application network, complexity, etc. Experiments are conducted on both real and simulated datasets, comparing the performance of several representative methods on indicators such as modularity, normalized mutual information, and adjustment of the Rand index. The work of multi-layer network community detection is summarized and prospected.

Keywords

• community detection
• multi-layer network
• modularity
• complex network analysis