Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capability of deep learning, deep graph clustering methods have achieved great success in recent years. However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field. From this motivation, we conduct a comprehensive survey of deep graph clustering. Firstly, we introduce formulaic definition, evaluation, and development in this field. Secondly, the taxonomy of deep graph clustering methods is presented based on four different criteria, including graph type, network architecture, learning paradigm, and clustering method. Thirdly, we carefully analyze the existing methods via extensive experiments and summarize the challenges and opportunities from five perspectives, including graph data quality, stability, scalability, discriminative capability, and unknown cluster number. Besides, the applications of deep graph clustering methods in six domains, including computer vision, natural language processing, recommendation systems, social network analyses, bioinformatics, and medical science, are presented. Last but not least, this paper provides open resource supports, including 1) a collection (\url{https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering}) of state-of-the-art deep graph clustering methods (papers, codes, and datasets) and 2) a unified framework (\url{https://github.com/Marigoldwu/A-Unified-Framework-for-Deep-Attribute-Graph-Clustering}) of deep graph clustering. We hope this work can serve as a quick guide and help researchers overcome challenges in this vibrant field.

翻译：图聚类旨在将图中的节点划分为若干不同的簇，这是一项基础且具有挑战性的任务。得益于深度学习强大的表征能力，深度图聚类方法近年来取得了巨大成功。然而，相应的综述论文相对匮乏，对该领域进行总结迫在眉睫。基于此，我们对深度图聚类进行了全面综述。首先，我们介绍了该领域的公式化定义、评估标准及发展历程。其次，我们基于图类型、网络架构、学习范式和聚类方法四个不同标准，提出了深度图聚类方法的分类体系。第三，我们通过大量实验仔细分析了现有方法，并从图数据质量、稳定性、可扩展性、判别能力和未知簇数量五个角度总结了挑战与机遇。此外，本文还介绍了深度图聚类方法在计算机视觉、自然语言处理、推荐系统、社交网络分析、生物信息学和医学六个领域的应用。最后但同样重要的是，本文提供了开放资源支持，包括：1) 前沿深度图聚类方法的合集（论文、代码和数据集）（\url{https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering}）；2) 深度图聚类的统一框架（\url{https://github.com/Marigoldwu/A-Unified-Framework-for-Deep-Attribute-Graph-Clustering}）。我们希望这项工作能作为快速指南，帮助研究人员克服这个充满活力领域的挑战。