  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.
