mlsanityData to 7 Oct 2026

Relational Graph Convolutional Networks: A Closer Look

arXiv 2107.10015 ↗First posted 21 Jul 2021cs.LGcited by 8

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Abstract (arXiv)

In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks. Our explanation provides a friendly understanding of the different components of the RGCN for both users and researchers extending the RGCN approach. Furthermore, we introduce two new configurations of the RGCN that are more parameter efficient. The code and datasets are available at https://github.com/thiviyanT/torch-rgcn.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

21 of 45 references.

References · 21

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  8. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  9. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  10. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  11. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  12. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
9 more
  1. 2020Knowledge Graph Embedding for Link Prediction: A Comparative Analysiscited by 85
  2. 2019Relational Graph Attention Networkscited by 81
  3. 2016Theano: A Python framework for fast computation of mathematical expressionscited by 64
  4. 2020Evaluating Logical Generalization in Graph Neural Networkscited by 12
  5. 2019Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretationcited by 11
  6. 2020Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networkscited by 8
  7. 2020Message Passing Query Embeddingcited by 8
  8. 2017Heterogeneous Information Network Embedding for Meta Path based Proximitycited by 5
  9. 2018Counting Motifs with Graph Samplingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 0; 2024: 0; 2025: 2; 2026: 1.

8 papers.

  1. 2022GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendationscited by 22
  2. 2022R-GCN: The R Could Stand for Randomcited by 9
  3. 2022Scaling R-GCN Training with Graph Summarizationcited by 5
  4. 2025BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactionscited by 4
  5. 2022Ensemble Multi-Relational Graph Neural Networkscited by 3
  6. 2022A Unification Framework for Euclidean and Hyperbolic Graph Neural Networkscited by 2
  7. 2026Incorporating Deep Learning Design in Database Queriescited by 1
  8. 2025gHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphscited by 0