review community detection algorithms in multilayer networks; Traditional methods and deep learning

Document Type : Original Article

Authors

1 Department of Computer Engineering, National University of Skills (NUS), Tehran ,

2 Professor, Department of Industrial Engineering, University if Qom, Qom, Iran. Email: j.rezaee@qom.ac.ir

Abstract

A community in networks is usually considered as a group of nodes that are more connected among their members than other members of the network. community detection algorithms are fundamental tools that allow us to discover organizational principles in networks. Today, with the ever-increasing growth of data and the complexity of their structure, data are modeled as multilayer networks. community detection in multilayer networks is one of the key issues in the field of data processing. In this research, more than 50 community detection algorithms multilayer networks have been investigated. We have examined these methods in two main categories: traditional methods and deep learning methods. After a complete review of the methods, according to their advantages and disadvantages, the main challenges in this field have been identified. community detection in directed multilayer networks, finding overlapping communities in dynamic networks and providing scalable algorithms have been among the most important challenges identified in this field. According to these challenges, suggestions have been made to develop methods to overcome the disadvantages of the current algorithms.

Keywords

Main Subjects

References

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