Data to 14 Oct 2026

Relational Graph Attention Networks

arXiv 1904.05811 ↗First posted 11 Apr 2019cs.LG cs.AI stat.MLcited by 82

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Abstract (arXiv)

We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established benchmarks. To provide a meaningful comparison, we retrain Relational Graph Convolutional Networks, the spectral counterpart of Relational Graph Attention Networks, and evaluate them under the same conditions. We find that Relational Graph Attention Networks perform worse than anticipated, although some configurations are marginally beneficial for modelling molecular properties. We provide insights as to why this may be, and suggest both modifications to evaluation strategies, as well as directions to investigate for future work.

Key facts. Cited by 82 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Apr 2019.

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Citing papers per year: 2021: 10; 2022: 19; 2023: 19; 2024: 13; 2025: 9; 2026: 12.

82 papers.

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2021How Attentive are Graph Attention Networks?cited by 835
2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 264
2022Graph neural networks for materials science and chemistrycited by 182
2024KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoningcited by 60
2021Bag of Tricks for Node Classification with Graph Neural Networkscited by 40
2023Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applicationscited by 33
2021Semi-Supervised Variational Reasoning for Medical Dialogue Generationcited by 30
2023IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Researchcited by 30
2022VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answeringcited by 22
2022Protein Language Models and Structure Prediction: Connection and Progressioncited by 17
2023GFS: Graph-based Feature Synthesis for Prediction over Relational Databasescited by 16
2022Multimodal Learning on Graphs for Disease Relation Extractioncited by 15

38 more

2021Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classificationcited by 13
2021Neural Trees for Learning on Graphscited by 13
2021Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Productioncited by 11
20244DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBscited by 11
2026No Need to Train Your RDB Foundation Modelcited by 10
2021r-GAT: Relational Graph Attention Network for Multi-Relational Graphscited by 9
2022Medical Dialogue Response Generation with Pivotal Information Recallingcited by 9
2023Explainable Action Prediction through Self-Supervision on Scene Graphscited by 9
2024Structure Guided Large Language Model for SQL Generationcited by 9
2024EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamicscited by 9
2021Relational Graph Convolutional Networks: A Closer Lookcited by 8
2022Learnable Graph Convolutional Attention Networkscited by 8
2026LUMINA: Foundation Models for Topology Transferable ACOPFcited by 8
2022Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformercited by 7
2024GraphStorm: all-in-one graph machine learning framework for industry applicationscited by 7
2022Distilling Multi-Scale Knowledge for Event Temporal Relation Extractioncited by 6
2022Mitigating Relational Bias on Knowledge Graphscited by 6
2023RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detectioncited by 5
2023Unsupervised Pretraining for Fact Verification by Language Model Distillationcited by 5
2023Dynamic Relation-Attentive Graph Neural Networks for Fraud Detectioncited by 5
2026RDBLearn: Simple In-Context Prediction Over Relational Databasescited by 5
2021Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networkscited by 4
2022CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversationscited by 4
2023DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networkscited by 4
2023Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networkscited by 4
2026Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfindingcited by 4
2022Enhancing Document-level Relation Extraction by Entity Knowledge Injectioncited by 3
2022Graph Neural Modeling of Network Flowscited by 3
2022EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Datacited by 3
2023LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehensioncited by 3
2022Search to Pass Messages for Temporal Knowledge Graph Completioncited by 2
2022Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networkscited by 2
2023Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentationscited by 2
2023Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphscited by 2
2023MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Documentcited by 2
2024Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learningcited by 2
2024Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approachescited by 2
2024Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inferencecited by 2

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