mlsanityData to 14 Oct 2026

Graph-based Semi-supervised Learning: A Comprehensive Review

arXiv 2102.13303 ↗First posted 26 Feb 2021cs.LGcited by 79

Not on the map. Why?

Abstract (arXiv)

Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to naturally represent data as graphs such that the label information of unlabelled samples can be inferred from the graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large scale data. Focusing on this class of methods, this work aims to provide both researchers and practitioners with a solid and systematic understanding of relevant advances as well as the underlying connections among them. This makes our paper distinct from recent surveys that cover an overall picture of SSL methods while neglecting fundamental understanding of GSSL methods. In particular, a major contribution of this paper lies in a new generalized taxonomy for GSSL, including graph regularization and graph embedding methods, with the most up-to-date references and useful resources such as codes, datasets, and applications. Furthermore, we present several potential research directions as future work with insights into this rapidly growing field.

Key facts. Cited by 79 papers in the map (data through 14 Oct 2026). Builds on 73 of its 210 references that are in the map. First posted on arXiv 26 Feb 2021.

Builds on

73 of 210 references.

References · 73

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  8. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  9. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  10. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  11. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  12. 2019Simplifying Graph Convolutional Networkscited by 1,477
38 more
  1. 2017Deep Setscited by 1,421
  2. 2016Variational Graph Auto-Encoderscited by 1,382
  3. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  4. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  5. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  6. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  7. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
  8. 2015Gated Graph Sequence Neural Networkscited by 796
  9. 2019Heterogeneous Graph Attention Networkcited by 734
  10. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  11. 2015LINE: Large-scale Information Network Embeddingcited by 582
  12. 2017Representation Learning on Graphs: Methods and Applicationscited by 457
  13. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  14. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  15. 2019Label Propagation for Deep Semi-supervised Learningcited by 289
  16. 2020Graph Random Neural Network for Semi-Supervised Learning on Graphscited by 213
  17. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 202
  18. 2018Learning a SAT Solver from Single-Bit Supervisioncited by 200
  19. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 198
  20. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  21. 2017Predicting multicellular function through multi-layer tissue networkscited by 138
  22. 2020An Overview of Deep Semi-Supervised Learningcited by 121
  23. 2020Towards Deeper Graph Neural Networks with Differentiable Group Normalizationcited by 114
  24. 2018Attention-based Graph Neural Network for Semi-supervised Learningcited by 101
  25. 2018Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputscited by 98
  26. 2015PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networkscited by 96
  27. 2020Uncertainty Aware Semi-Supervised Learning on Graph Datacited by 95
  28. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 89
  29. 2020Scalable Graph Neural Networks via Bidirectional Propagationcited by 87
  30. 2019Learning Graph Embedding with Adversarial Training Methodscited by 84
  31. 2019Stability and Generalization of Graph Convolutional Neural Networkscited by 81
  32. 2018Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoderscited by 74
  33. 2020Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Modelcited by 59
  34. 2017HARP: Hierarchical Representation Learning for Networkscited by 58
  35. 2020Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networkscited by 57
  36. 2018N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classificationcited by 54
  37. 2020Graph Convolutional Networks using Heat Kernel for Semi-supervised Learningcited by 51
  38. 2020Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Ratescited by 49

50 most cited of 73 All 73 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 7; 2022: 17; 2023: 18; 2024: 17; 2025: 14; 2026: 6.

79 papers.

Sort:

By area:

  1. 2021A Survey on Deep Semi-supervised Learningcited by 251
  2. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 103
  3. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
  4. 2022COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learningcited by 39
  5. 2022HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularizationcited by 34
  6. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  7. 2021Few-Shot Object Detection: A Comprehensive Surveycited by 29
  8. 2023Class-Imbalanced Graph Learning without Class Rebalancingcited by 29
  9. 2024Towards Effective and General Graph Unlearning via Mutual Evolutioncited by 29
  10. 2021Imbalanced Graph Classification via Graph-of-Graph Neural Networkscited by 27
  11. 2024Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representationcited by 26
  12. 2023Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignmentcited by 25
38 more
  1. 2022HICF: Hyperbolic Informative Collaborative Filteringcited by 24
  2. 2023Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classificationcited by 19
  3. 2023Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learningcited by 16
  4. 2024Rethinking Node-wise Propagation for Large-scale Graph Learningcited by 16
  5. 2022kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learningcited by 14
  6. 2024Graph Neural Networks in Histopathology: Emerging Trends and Future Directionscited by 13
  7. 2022Graph-adaptive Rectified Linear Unit for Graph Neural Networkscited by 12
  8. 2021Multimodality in Meta-Learning: A Comprehensive Surveycited by 11
  9. 2022Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcitycited by 11
  10. 2025CGMatch: A Different Perspective of Semi-supervised Learningcited by 11
  11. 2023Machine Learning for Urban Air Quality Analytics: A Surveycited by 8
  12. 2021Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorialcited by 6
  13. 2022Graph Component Contrastive Learning for Concept Relatedness Estimationcited by 6
  14. 2023From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisitedcited by 6
  15. 2023Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumptioncited by 6
  16. 2024A Unified Data Representation Learning for Non-parametric Two-sample Testingcited by 6
  17. 2025Prediction-Powered Semi-Supervised Learning with Online Power Tuningcited by 5
  18. 2023Local Structure-aware Graph Contrastive Representation Learningcited by 4
  19. 2024Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformercited by 4
  20. 2025Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularizationcited by 4
  21. 2022Label Efficient Regularization and Propagation for Graph Node Classificationcited by 3
  22. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
  23. 2025Aggregation Buffer: Revisiting DropEdge with a New Parameter Blockcited by 3
  24. 2023Persistent Laplacian-enhanced Algorithm for Scarcely Labeled Data Classificationcited by 2
  25. 2023Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signalscited by 2
  26. 2024Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networkscited by 2
  27. 2024Improved Graph-based semi-supervised learning Schemescited by 2
  28. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  29. 2025Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approachcited by 2
  30. 2025Graph Your Own Promptcited by 2
  31. 2021A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detectioncited by 1
  32. 2022Learning Label Initialization for Time-Dependent Harmonic Extensioncited by 1
  33. 2022Semi-supervised Learning with Deterministic Labeling and Large Margin Projectioncited by 1
  34. 2022Leveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibrationcited by 1
  35. 2022Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classificationcited by 1
  36. 2024A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detectioncited by 1
  37. 2024MALADY: Multiclass Active Learning with Auction Dynamics on Graphscited by 1
  38. 2025AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Modelscited by 1

50 most cited of 79 All 79 (JSON)