mlsanityData to 7 Oct 2026

Modeling Relational Data with Graph Convolutional Networks

arXiv 1703.06103 ↗First posted 17 Mar 2017stat.ML cs.AI cs.DBcited by 1,684

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Cited by 49 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to deal with the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved by enriching them with an encoder model to accumulate evidence over multiple inference steps in the relational graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.

Key facts. Cited by 1,684 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 302; 2022: 355; 2023: 352; 2024: 278; 2025: 209; 2026: 188.

1,684 papers.

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  1. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  2. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  3. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  4. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  5. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  6. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  7. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 203
  8. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  9. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  10. 2022Graph neural networks for materials science and chemistrycited by 181
  11. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
  12. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
38 more
  1. 2022GreaseLM: Graph REASoning Enhanced Language Models for Question Answeringcited by 170
  2. 2022Protein Representation Learning by Geometric Structure Pretrainingcited by 161
  3. 2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
  4. 2024GraphRouter: A Graph-based Router for LLM Selectionscited by 154
  5. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  6. 2021Graph Learning: A Surveycited by 140
  7. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  8. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  9. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  10. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  11. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  12. 2022HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encodingcited by 109
  13. 2021Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learningcited by 106
  14. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  15. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  16. 2022FoSR: First-order spectral rewiring for addressing oversquashing in GNNscited by 104
  17. 2021MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversationcited by 99
  18. 2021Directed Acyclic Graph Network for Conversational Emotion Recognitioncited by 97
  19. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  20. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  21. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  22. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  23. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  24. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  25. 2021Knowledge Graph Reasoning with Relational Digraphcited by 91
  26. 2024KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graphcited by 90
  27. 2024RelBench: A Benchmark for Deep Learning on Relational Databasescited by 90
  28. 2023Edge Directionality Improves Learning on Heterophilic Graphscited by 88
  29. 2022GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversationcited by 86
  30. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  31. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  32. 2021The Logic of Graph Neural Networkscited by 84
  33. 2024Position: Graph Foundation Models are Already Herecited by 84
  34. 2022Graph Attention Multi-Layer Perceptroncited by 82
  35. 2023DRew: Dynamically Rewired Message Passing with Delaycited by 81
  36. 2024Scientific Large Language Models: A Survey on Biological & Chemical Domainscited by 80
  37. 2021Graph Representation Learning in Biomedicinecited by 79
  38. 2021OOD-GNN: Out-of-Distribution Generalized Graph Neural Networkcited by 78

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