  Complex networks contain various interactions among similar or different
entities. These kinds of networks are called multi-relational networks, in
which each layer corresponds to a special type of interaction. Multi-relational
networks are a particular type of multilayer networks in which nodes are
similar entities; however, edges or communications demonstrate different types
of interactions among similar entities. In this survey, we study community
detection methods for multi-relational networks. The considered models are
divided into two main groups, namely, direct methods and indirect methods. We
put indirect methods in two classes: flattening and ensembling, and the direct
methods are further divided into four groups which are: probabilistic methods,
algebraic methods, modular-based methods, and graph feature-based methods. For
each approach and each method, we explain their pros and cons. Additionally,
all the used datasets in the multilayer community detection studies are
categorized into synthetic and real data. We elaborate on the most important
datasets. Afterward, the utilized evaluation metrics by the papers are
described. Finally, the current models' challenges and shortcomings are
discussed. Finally, some suggestions for future research are developed. Putting
all this together, this study, to the best of our knowledge, is the most
comprehensive survey dedicated to multi-relational networks community
detection.
