mlsanityData to 7 Oct 2026

r-GAT: Relational Graph Attention Network for Multi-Relational Graphs

arXiv 2109.05922 ↗First posted 13 Sep 2021cs.AI cs.CL cs.LGcited by 9

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Abstract (arXiv)

Graph Attention Network (GAT) focuses on modelling simple undirected and single relational graph data only. This limits its ability to deal with more general and complex multi-relational graphs that contain entities with directed links of different labels (e.g., knowledge graphs). Therefore, directly applying GAT on multi-relational graphs leads to sub-optimal solutions. To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations. Specifically, each channel corresponds to a latent semantic aspect of an entity. This enables us to aggregate neighborhood information for the current aspect using relation features. We further propose a query-aware attention mechanism for subsequent tasks to select useful aspects. Extensive experiments on link prediction and entity classification tasks show that our r-GAT can model multi-relational graphs effectively. Also, we show the interpretability of our approach by case study.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

21 of 38 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  6. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  7. 2016Complex Embeddings for Simple Link Predictioncited by 897
  8. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  9. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  10. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
  11. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 323
  12. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
9 more
  1. 2019Quaternion Knowledge Graph Embeddingscited by 175
  2. 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
  3. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  4. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  5. 2019Relational Graph Attention Networkscited by 81
  6. 2019A Re-evaluation of Knowledge Graph Completion Methodscited by 73
  7. 2019InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactionscited by 58
  8. 2018A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalizationcited by 30
  9. 2020Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classificationcited by 29

Built on by

Cited by year (2026 to 7 Oct)

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

9 papers.

  1. 2023GNNDelete: A General Strategy for Unlearning in Graph Neural Networkscited by 43
  2. 2022A Review of Knowledge Graph Completioncited by 8
  3. 2024Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learningcited by 2
  4. 2023Towards a GML-Enabled Knowledge Graph Platformcited by 1
  5. 2024MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributescited by 1
  6. 2025Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA modelcited by 1
  7. 2025Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approachcited by 0
  8. 2026Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networkscited by 0
  9. 2026Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passingcited by 0