A survey of community detection methods in multilayer networks
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A survey of community detection methods in multilayer networks Xinyu Huang1 · Dongming Chen1
· Tao Ren1 · Dongqi Wang1
Received: 24 February 2020 / Accepted: 10 September 2020 © The Author(s) 2020
Abstract 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. Keywords Community detection · Multilayer network · Temporal network · Multiplex network · Multilevel network
Responsible editor: Tim Weninger.
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Dongming Chen [email protected] Xinyu Huang [email protected] Tao Ren [email protected] Dongqi Wang [email protected]
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Software College, Northeastern University, Shenyang 110169, China
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X. Huang et al.
1 Introduction Network theory is an important tool for describing and analyzing complex systems throughout a variety of disciplines. Community structures, defined as groups of nodes that are more densely connected than with rest of the network, are widely existed in many real-world complex systems, such as sociology, biology, transportation systems, and so on (Newman 2018). Discovering communities in these systems has become a primary approach to understand how network structure relates to system behaviors. As an effective technique to unveil the underlying structures, community detection has been utilized in many scenarios, such as finding potential friends in social media (Zhu et al. 2017), recommending products for users (Li and Zhang 2020), analyzing social opinions (Wang et al. 2017), and so on. With the deepening of research, more and more scholars come to realize that simply uncovering communities in a single network is insufficient to analyze the structures and system behaviors in real-life applications. Unlike the community structure in single-layer networks, communities in multilayer networks are comprised of a group of well-connected nodes in all layers. For example, individuals in social networks may have various interactions (e.g. sending emails, participating in the same activity) among them (Ansari et al. 2011). As a result, the conventional studies are encountering with an essential problem of how to utilize the multiple views of the network (Papalexakis et al. 2013). There are also similar scenario
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