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Graph Random Neural Network for Semi-Supervised Learning on Graphs

arXiv 2005.11079 ↗First posted 22 May 2020cs.LG cs.SI stat.MLcited by 213

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Abstract (arXiv)

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework -- GRAPH RANDOM NEURAL NETWORKS (GRAND) -- to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https://github.com/Grand20/grand.

Key facts. Cited by 213 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 May 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 37; 2022: 44; 2023: 47; 2024: 43; 2025: 27; 2026: 15.

213 papers.

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  1. 2021A Survey on Deep Semi-supervised Learningcited by 251
  2. 2023Language is All a Graph Needscited by 220
  3. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  4. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
  5. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
  6. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 132
  7. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 118
  8. 2021Dirichlet Energy Constrained Learning for Deep Graph Neural Networkscited by 97
  9. 2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
  10. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  11. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  12. 2021Learning from Counterfactual Links for Link Predictioncited by 69
38 more
  1. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 67
  2. 2023Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothingcited by 67
  3. 2022A Survey on Graph Representation Learning Methodscited by 66
  4. 2021Reconstruction for Powerful Graph Representationscited by 65
  5. 2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63
  6. 2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 62
  7. 2022Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented Viewcited by 55
  8. 2021Local Augmentation for Graph Neural Networkscited by 53
  9. 2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
  10. 2024A Comprehensive Survey on Data Augmentationcited by 40
  11. 2022Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queriescited by 39
  12. 2023Are Defenses for Graph Neural Networks Robust?cited by 38
  13. 2021Edge Representation Learning with Hypergraphscited by 37
  14. 2021Tree Decomposed Graph Neural Networkcited by 35
  15. 2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
  16. 2023GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentationcited by 31
  17. 2022Metropolis-Hastings Data Augmentation for Graph Neural Networkscited by 30
  18. 2022On the Robustness of Graph Neural Diffusion to Topology Perturbationscited by 30
  19. 2022DropMessage: Unifying Random Dropping for Graph Neural Networkscited by 29
  20. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  21. 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
  22. 2021Graph Pointer Neural Networkscited by 27
  23. 2021Imbalanced Graph Classification via Graph-of-Graph Neural Networkscited by 27
  24. 2022Model Degradation Hinders Deep Graph Neural Networkscited by 26
  25. 2022GRAND+: Scalable Graph Random Neural Networkscited by 25
  26. 2023Learning Strong Graph Neural Networks with Weak Informationcited by 25
  27. 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approachcited by 24
  28. 2023Learning How to Propagate Messages in Graph Neural Networkscited by 23
  29. 2023Data-centric Graph Learning: A Surveycited by 22
  30. 2022Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learningcited by 20
  31. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 19
  32. 2021Evaluating Deep Graph Neural Networkscited by 19
  33. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  34. 2023GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Expertscited by 19
  35. 2024Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learningcited by 18
  36. 2022Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervisioncited by 16
  37. 2023Graph Mixup with Soft Alignmentscited by 15
  38. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15

50 most cited of 213 All 213 (JSON)