mlsanityData to 14 Oct 2026

Deep Learning for Community Detection: Progress, Challenges and Opportunities

arXiv 2005.08225 ↗First posted 17 May 2020cs.SI cs.AI cs.LGcited by 42

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Abstract (arXiv)

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2020.

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Citing papers per year: 2021: 12; 2022: 13; 2023: 9; 2024: 6; 2025: 2.

42 papers.

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  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  3. 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
  4. 2021A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learningcited by 55
  5. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  6. 2021Graph Communal Contrastive Learningcited by 34
  7. 2024Graph Transformers: A Surveycited by 32
  8. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  9. 2022GLCC: A General Framework for Graph-Level Clusteringcited by 21
  10. 2022Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspectivecited by 19
  11. 2021A Survey on Deep Learning Event Extraction: Approaches and Applicationscited by 12
  12. 2022Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Predictioncited by 12
30 more
  1. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  2. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  3. 2023A Comprehensive Review of Community Detection in Graphscited by 12
  4. 2022Curvature Graph Generative Adversarial Networkscited by 9
  5. 2023Redundancy-Free Self-Supervised Relational Learning for Graph Clusteringcited by 8
  6. 2021Transferring Knowledge Distillation for Multilingual Social Event Detectioncited by 7
  7. 2021Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learningcited by 7
  8. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  9. 2021Event Extraction by Associating Event Types and Argument Rolescited by 5
  10. 2024Relational Prompt-based Pre-trained Language Models for Social Event Detectioncited by 5
  11. 2021A Survey on Role-Oriented Network Embeddingcited by 4
  12. 2023A Comprehensive Survey on Graph Summarization with Graph Neural Networkscited by 4
  13. 2023Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detectioncited by 4
  14. 2023Contrastive Deep Nonnegative Matrix Factorization for Community Detectioncited by 4
  15. 2024Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphscited by 4
  16. 2023Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detectioncited by 3
  17. 2021Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discoverycited by 1
  18. 2021Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networkscited by 1
  19. 2021A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detectioncited by 1
  20. 2022Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmapcited by 1
  21. 2022Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learningcited by 1
  22. 2022Unsupervised Domain Adaptation with Implicit Pseudo Supervision for Semantic Segmentationcited by 1
  23. 2023A clustering and graph deep learning-based framework for COVID-19 drug repurposingcited by 1
  24. 2024Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Mediacited by 1
  25. 2024Advanced Graph Clustering Methods: A Comprehensive and In-Depth Analysiscited by 1
  26. 2025Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependenciescited by 1
  27. 2022Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learningcited by 0
  28. 2023Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspectivecited by 0
  29. 2024Unsupervised Optimisation of GNNs for Node Clusteringcited by 0
  30. 2025Fast Clustering of Categorical Big Datacited by 0