mlsanityData to 14 Oct 2026

Graph-Revised Convolutional Network

arXiv 1911.07123 ↗First posted 17 Nov 2019cs.LG stat.MLcited by 59

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Abstract (arXiv)

Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice in most GCNs, unavoidably leads to sub-optimal solutions. Existing efforts for addressing this problem either involve an over-parameterized model which is difficult to scale, or simply re-weight observed edges without dealing with the missing-edge issue. This paper proposes a novel framework called Graph-Revised Convolutional Network (GRCN), which avoids both extremes. Specifically, a GCN-based graph revision module is introduced for predicting missing edges and revising edge weights w.r.t. downstream tasks via joint optimization. A theoretical analysis reveals the connection between GRCN and previous work on multigraph belief propagation. Experiments on six benchmark datasets show that GRCN consistently outperforms strong baseline methods by a large margin, especially when the original graphs are severely incomplete or the labeled instances for model training are highly sparse.

Key facts. Cited by 59 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 7; 2022: 12; 2023: 13; 2024: 13; 2025: 6; 2026: 8.

59 papers.

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  1. 2022Towards Unsupervised Deep Graph Structure Learningcited by 114
  2. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 110
  3. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  4. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  5. 2023GSLB: The Graph Structure Learning Benchmarkcited by 50
  6. 2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
  7. 2024GraphEdit: Large Language Models for Graph Structure Learningcited by 33
  8. 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learningcited by 30
  9. 2021Recognizing Predictive Substructures with Subgraph Information Bottleneckcited by 28
  10. 2023Probabilistically Rewired Message-Passing Neural Networkscited by 26
  11. 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreachingcited by 24
  12. 2023Data-centric Graph Learning: A Surveycited by 22
38 more
  1. 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactionscited by 21
  2. 2024Probabilistic Graph Rewiring via Virtual Nodescited by 15
  3. 2022Deep Graph Learning for Anomalous Citation Detectioncited by 14
  4. 2021GraphFM: Graph Factorization Machines for Feature Interaction Modelingcited by 13
  5. 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learningcited by 12
  6. 2024Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classificationcited by 11
  7. 2022Finding Diverse and Predictable Subgraphs for Graph Domain Generalizationcited by 9
  8. 2023UGSL: A Unified Framework for Benchmarking Graph Structure Learningcited by 9
  9. 2023Latent Graph Inference with Limited Supervisioncited by 9
  10. 2024RobGC: Towards Robust Graph Condensationcited by 8
  11. 2022Spatio-Temporal Latent Graph Structure Learning for Traffic Forecastingcited by 7
  12. 2024Deep Contrastive Graph Learning with Clustering-Oriented Guidancecited by 7
  13. 2023Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphscited by 6
  14. 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margincited by 6
  15. 2022DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learningcited by 5
  16. 2022Knowledge-Aware Bayesian Deep Topic Modelcited by 5
  17. 2021A Comparative Study on Robust Graph Neural Networks to Structural Noisescited by 4
  18. 2024Learning Latent Graph Structures and their Uncertaintycited by 4
  19. 2025Uncertainty-Aware Graph Structure Learningcited by 4
  20. 2025Unsupervised Graph Clustering with Deep Structural Entropycited by 4
  21. 2021On Generalization of Graph Autoencoders with Adversarial Trainingcited by 3
  22. 2023TabGSL: Graph Structure Learning for Tabular Data Predictioncited by 3
  23. 2024Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learningcited by 3
  24. 2025Graph Defense Diffusion Modelcited by 3
  25. 2021GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networkscited by 2
  26. 2022ASGNN: Graph Neural Networks with Adaptive Structurecited by 2
  27. 2024GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extractioncited by 2
  28. 2024GraphRevisedIE: Multimodal Information Extraction with Graph-Revised Networkcited by 2
  29. 2022HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detectioncited by 1
  30. 2024Motif-driven Subgraph Structure Learning for Graph Classificationcited by 1
  31. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  32. 2026Dynamic Graph Structure Learning via Resistance Curvature Flowcited by 1
  33. 2022Multi-view graph structure learning using subspace merging on Grassmann manifoldcited by 0
  34. 2022GPN: A Joint Structural Learning Framework for Graph Neural Networkscited by 0
  35. 2022Learning the Network of Graphs for Graph Neural Networkscited by 0
  36. 2023Graph Structural Residuals: A Learning Approach to Diagnosiscited by 0
  37. 2023Resist Label Noise with PGM for Graph Neural Networkscited by 0
  38. 2023Neural Gaussian Similarity Modeling for Differential Graph Structure Learningcited by 0

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