A survey of community detection methods in multilayer networks

Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there's an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

Automated summary

Community detection is a vital research area focusing on the connectivity of nodes in complex systems. Recently, there has been a growing interest in the development of multilayer networks. Most traditional algorithms struggle to perform well in multilayer networks, hence this paper compares existing works and provides an analysis of representative algorithms to enhance the understanding of community detection methods in multilayer networks. The comparison results suggest that there is a need for improved algorithm efficiency and more general approaches in future research.