A comprehensive review of community detection in graphs

Jiakang Li, Songning Lai, Zhihao Shuai, Yuan Tan, Yifan Jia, Mianyang Yu, Zichen Song, Xiaokang Peng, Ziyang Xu, Yongxin Ni, Haifeng Qiu, Jiayu Yang, Yutong Liu, Yonggang Lu

Abstract: Highlights•This review article provides a comprehensive exploration of the community detection methods which are grouped into modularity, spectral clustering, probabilistic modeling, and deep learning based methods.•The review contributes to a deep understanding of community detection in graphs and enhances our knowledge of network dynamics in complex systems.•The review also introduces a new community detection method, the Revised Medoid-Shift (RMS), designed to improve performance on non-Euclidean data structures like graphs.