# Edge-Featured Graph Attention Network

arXiv: https://arxiv.org/abs/2101.07671 · HTML: https://mlsanity.com/paper/2101.07671/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)

## Built on by (most cited first, 18 of 18)

- [G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems](https://mlsanity.com/paper/2502.11127/) (2025)
- [GREAT: A Graph Edge Attention Network for the Electric Vehicle Routing Problem with Asymmetric Costs](https://mlsanity.com/paper/2408.16717/) (2024)
- [INFA-Guard: Mitigating Malicious Propagation via Infection-Aware Safeguarding in LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2601.14667/) (2026)
- [Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery](https://mlsanity.com/paper/2106.02190/) (2021)
- [EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems](https://mlsanity.com/paper/2506.16428/) (2025)
- [Neural Improvement Heuristics for Graph Combinatorial Optimization Problems](https://mlsanity.com/paper/2206.00383/) (2022)
- [RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design](https://mlsanity.com/paper/2305.11699/) (2023)
- [Edge-featured Graph Neural Architecture Search](https://mlsanity.com/paper/2109.01356/) (2021)
- [Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses](https://mlsanity.com/paper/2201.03229/) (2022)
- [Triplet Edge Attention for Algorithmic Reasoning](https://mlsanity.com/paper/2312.05611/) (2023)
- [Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions](https://mlsanity.com/paper/2407.12419/) (2024)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [The Return of Structural Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2508.19773/) (2025)
- [Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics](https://mlsanity.com/paper/2510.25683/) (2025)
- [RE2: Region-Aware Relation Extraction from Visually Rich Documents](https://mlsanity.com/paper/2305.14590/) (2023)
- [ESGCN: Edge Squeeze Attention Graph Convolutional Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2307.01227/) (2023)
- [Enhancing Chess Reinforcement Learning with Graph Representation](https://mlsanity.com/paper/2410.23753/) (2024)
- [Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph](https://mlsanity.com/paper/2411.04907/) (2024)

## Abstract (arXiv)

Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models. In this paper, we present edge-featured graph attention networks, namely EGATs, to extend the use of graph neural networks to those tasks learning on graphs with both node and edge features. These models can be regarded as extensions of graph attention networks (GATs). By reforming the model structure and the learning process, the new models can accept node and edge features as inputs, incorporate the edge information into feature representations, and iterate both node and edge features in a parallel but mutual way. The results demonstrate that our work is highly competitive against other node classification approaches, and can be well applied in edge-featured graph learning tasks.