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Graph clustering, which aims to divide nodes in the graph into severaldistinct clusters, is a fundamental yet challenging task. Benefiting from thepowerful representation capability of deep learning, deep graph clusteringmethods have achieved great success in recent years. However, the correspondingsurvey paper is relatively scarce, and it is imminent to make a summary of thisfield. From this motivation, we conduct a comprehensive survey of deep graphclustering. Firstly, we introduce formulaic definition, evaluation, anddevelopment in this field. Secondly, the taxonomy of deep graph clusteringmethods is presented based on four different criteria, including graph type,network architecture, learning paradigm, and clustering method. Thirdly, wecarefully analyze the existing methods via extensive experiments and summarizethe challenges and opportunities from five perspectives, including graph dataquality, stability, scalability, discriminative capability, and unknown clusternumber. Besides, the applications of deep graph clustering methods in sixdomains, including computer vision, natural language processing, recommendationsystems, social network analyses, bioinformatics, and medical science, arepresented. Last but not least, this paper provides open resource supports,including 1) a collection(\url{https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering}) ofstate-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 helpresearchers overcome challenges in this vibrant field.

A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource