A survey of community detection methods in multilayer networks

• Published in: Data mining and knowledge discovery Vol. 35; no. 1; pp. 1 - 45
• Main Authors: Huang, Xinyu, Chen, Dongming, Ren, Tao, Wang, Dongqi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Academic Surveys and Tutorials; Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Complex systems; Computer Science; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Monolayers; Multilayers; Networks; Nodes; Physics; Sociology; Statistics for Engineering; Community detection; Multiplex network; Multilevel network; Temporal network; Multilayer network
• ISSN: 1384-5810; 1573-756X; 1573-756X
• DOI: 10.1007/s10618-020-00716-6

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.