mlsanityData to 7 Oct 2026

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

arXiv 2211.12875 ↗First posted 23 Nov 2022cs.LG cs.AIcited by 26

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Abstract (arXiv)

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 (urlhttps://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 (urlhttps://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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 86 of its 199 references that are in the map. First posted on arXiv 23 Nov 2022.

Builds on

86 of 199 references.

References · 86

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Citing papers per year: 2023: 2; 2024: 6; 2025: 8; 2026: 10.

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  1. 2024Reliable Node Similarity Matrix Guided Contrastive Graph Clusteringcited by 12
  2. 2023One-step Multi-view Clustering with Diverse Representationcited by 10
  3. 2024End-to-end Learnable Clustering for Intent Learning in Recommendationcited by 10
  4. 2023SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoningcited by 9
  5. 2024Spectral Subspace Clustering for Attributed Graphscited by 6
  6. 2024THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplingscited by 5
  7. 2025ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Spacecited by 4
  8. 2026Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clusteringcited by 3
  9. 2026KAT-Coder-V2.5 Technical Reportcited by 3
  10. 2024textttSynC: Synergistic Boosting of Structure and Representation for Deep Graph Clusteringcited by 2
  11. 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiationcited by 2
  12. 2025Effective Clustering for Large Multi-Relational Graphscited by 2
14 more
  1. 2025Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clusteringcited by 2
  2. 2025Community detection robustness of graph neural networkscited by 1
  3. 2026CLUBench: A Clustering Benchmarkcited by 1
  4. 2024Structure-enhanced Contrastive Learning for Graph Clusteringcited by 0
  5. 2025Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphscited by 0
  6. 2025Graph Neural Networks Powered by Encoder Embedding for Improved Node Learningcited by 0
  7. 2025Stochastic Deep Graph Clustering for Practical Group Formationcited by 0
  8. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 0
  9. 2026Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphscited by 0
  10. 2026Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learningcited by 0
  11. 2026Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Datacited by 0
  12. 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detectioncited by 0
  13. 2026Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approachcited by 0
  14. 2026Compactness and Consistency: A Conjoint Framework for Deep Graph Clusteringcited by 0