mlsanityData to 7 Oct 2026

Graph Learning: A Survey

arXiv 2105.00696 ↗First posted 3 May 2021cs.LG cs.AI cs.SIcited by 140

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Abstract (arXiv)

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.

Key facts. Cited by 140 papers in the map (data through 7 Oct 2026). Builds on 90 of its 190 references that are in the map. First posted on arXiv 3 May 2021.

Builds on

90 of 190 references.

References · 90

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  9. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  10. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  11. 2016Variational Graph Auto-Encoderscited by 1,373
  12. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
38 more
  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  2. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  3. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  5. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  6. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  7. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  8. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  9. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 600
  10. 2015LINE: Large-scale Information Network Embeddingcited by 582
  11. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 557
  12. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  13. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  14. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  15. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  16. 2018Deep Learning on Graphs: A Surveycited by 393
  17. 2018Graph Convolutional Networks for Text Classificationcited by 345
  18. 2016Learning Convolutional Neural Networks for Graphscited by 314
  19. 2015Diffusion-Convolutional Neural Networkscited by 268
  20. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  21. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  22. 2015Deep Convolutional Networks on Graph-Structured Datacited by 246
  23. 2017graph2vec: Learning Distributed Representations of Graphscited by 228
  24. 2018Learning Deep Generative Models of Graphscited by 227
  25. 2017Learning to Represent Programs with Graphscited by 219
  26. 2017struc2vec: Learning Node Representations from Structural Identitycited by 214
  27. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  28. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  29. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 164
  30. 2016How to learn a graph from smooth signalscited by 146
  31. 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
  32. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  33. 2018NetGAN: Generating Graphs via Random Walkscited by 129
  34. 2017GraphGAN: Graph Representation Learning with Generative Adversarial Netscited by 121
  35. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  36. 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 109
  37. 2015A Survey of Heterogeneous Information Network Analysiscited by 109
  38. 2017Network Representation Learning: A Surveycited by 101

50 most cited of 90 All 90 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 21; 2023: 23; 2024: 38; 2025: 35; 2026: 16.

140 papers.

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  1. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  2. 2022CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoningcited by 119
  3. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  4. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  5. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  6. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  7. 2022A Survey on Graph Representation Learning Methodscited by 66
  8. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  9. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 57
  10. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  11. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 49
  12. 2024Graph Transformers: A Surveycited by 32
38 more
  1. 2022Graph Lifelong Learning: A Surveycited by 30
  2. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  3. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  4. 2021Moving Object Detection for Event-based vision using Graph Spectral Clusteringcited by 28
  5. 2022Graph Neural Networks in IoT: A Surveycited by 27
  6. 2024Graph Condensation: A Surveycited by 23
  7. 2021Graph-based Deep Learning for Communication Networks: A Surveycited by 21
  8. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  9. 2023Permutation Equivariant Graph Framelets for Heterophilous Graph Learningcited by 19
  10. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  11. 2024Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmarkcited by 18
  12. 2023Continual Graph Learning: A Surveycited by 17
  13. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  14. 2024Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learningcited by 17
  15. 2021Towards Graph Self-Supervised Learning with Contrastive Adjusted Zoomingcited by 15
  16. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  17. 2022CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphscited by 14
  18. 2022Deep Graph Learning for Anomalous Citation Detectioncited by 14
  19. 2024Tensor-view Topological Graph Neural Networkcited by 12
  20. 2024Higher-order Structure Based Anomaly Detection on Attributed Networkscited by 12
  21. 2024Continual Learning on Graphs: A Surveycited by 11
  22. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  23. 2025Long-range Brain Graph Transformercited by 11
  24. 2023A Survey of Graph Prompting Methods: Techniques, Applications, and Challengescited by 10
  25. 2022Graph Augmentation Learningcited by 9
  26. 2023Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learningcited by 9
  27. 2023Topological Pooling on Graphscited by 9
  28. 2024Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 9
  29. 2022Unsupervised Network Embedding Beyond Homophilycited by 7
  30. 2023Quantum Graph Learning: Frontiers and Outlookcited by 6
  31. 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
  32. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  33. 2024A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literaturecited by 6
  34. 2024Deep Outdated Fact Detection in Knowledge Graphscited by 6
  35. 2021Sparse Graph Learning Under Laplacian-Related Constraintscited by 5
  36. 2022Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Frameworkcited by 5
  37. 2022MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learningcited by 5
  38. 2023RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA moleculescited by 5

50 most cited of 140 All 140 (JSON)