A Comprehensive Review of Community Detection in Graphs
Abstract
The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs.
Keywords
Cite
@article{arxiv.2309.11798,
title = {A Comprehensive Review of Community Detection in Graphs},
author = {Jiakang Li and Songning Lai and Zhihao Shuai and Yuan Tan and Yifan Jia and Mianyang Yu and Zichen Song and Xiaokang Peng and Ziyang Xu and Yongxin Ni and Haifeng Qiu and Jiayu Yang and Yutong Liu and Yonggang Lu},
journal= {arXiv preprint arXiv:2309.11798},
year = {2024}
}