Graph clustering, which aims to divide a graph into several homogeneous groups, is a critical area of study with applications that span various fields such as social network analysis, bioinformatics, and image segmentation. This paper explores both traditional and more recent approaches to graph clustering. Firstly, key concepts and definitions in graph theory are introduced. The background section covers essential topics, including graph Laplacians and the integration of Deep Learning in graph analysis. The paper then delves into traditional clustering methods, including Spectral Clustering and the Leiden algorithm. Following this, state-of-the-art clustering techniques that leverage deep learning are examined. A comprehensive comparison of these methods is made through experiments. The paper concludes with a discussion of the practical applications of graph clustering and potential future research directions.

翻译：图聚类旨在将图划分为若干同质组，是社交网络分析、生物信息学和图像分割等多个领域应用中的关键研究方向。本文系统探讨了传统及近期的图聚类方法。首先介绍了图论中的核心概念与定义。背景部分涵盖了图拉普拉斯矩阵及深度学习在图分析中的融合等基础主题。随后深入剖析了包括谱聚类与Leiden算法在内的传统聚类方法。继而考察了基于深度学习的前沿聚类技术。通过实验对这些方法进行了全面比较。最后讨论了图聚类的实际应用及未来潜在研究方向。