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.

翻译：复杂网络研究极大地增进了我们对社区结构的理解，而社区结构是真实世界图的重要特征。在图中检测社区是一个具有挑战性的问题，其应用涵盖社会学、生物学和计算机科学领域。尽管跨学科科学家群体付出了诸多努力，但这一问题尚未得到令人满意的解决方案。本文综述深入探讨了图社区检测的主题，系统阐述了基于模块度的方法、谱聚类、概率建模和深度学习等各类社区检测方法。除现有方法外，本文还介绍了一种我们自主设计的新型社区检测方法。此外，我们比较了这些方法在有/无真实标注数据集上的性能表现。最终，本综述为深入理解图社区检测提供了全面视角。