# How Attentive are Graph Attention Networks?

**Authors:** Shaked Brody, Uri Alon, Eran Yahav  
**arXiv:** [2105.14491](https://arxiv.org/abs/2105.14491)  
**Date:** 2021-05-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2105.14491v3/

## Key points

- Authors Shaked Brody, Uri Alon, and Eran Yahav discuss limitations of Graph Attention Networks (GATs), a popular architecture for graph neural networks (GNNs)
- GATs use a static attention mechanism that lacks expressiveness due to the ranking of attention scores not being conditioned on the query node
- This limitation leads to challenges in fitting training data effectively for certain graph problems
- Solution proposed: GATv2 - a dynamic graph attention variant that offers enhanced expressiveness by introducing dynamic attention capabilities
- GATv2 outperforms traditional GAT across multiple benchmarks including 11 OGB datasets while maintaining comparable parametric costs
- Significance of the work: Enhancing capabilities of graph neural networks by introducing dynamic attention mechanisms through GATv2, addressing limitations of static attention in GATs and opening up new possibilities for more effective representation learning with graphs
- Availability of code for replication and integration into popular libraries ensures widespread adoption and further research in this domain

## Summary

In their paper titled "How Attentive are Graph Attention Networks? ", authors Shaked Brody, Uri Alon, and Eran Yahav delve into the limitations of Graph Attention Networks (GATs), a popular architecture for graph neural networks (GNNs). GATs are widely regarded as state-of-the-art for representation learning with graphs. However, the authors identify a crucial drawback in their attention mechanism. They demonstrate that GATs employ a static attention mechanism where the ranking of attention scores is not conditioned on the query node, leading to limited expressiveness. This restricted form of attention, termed static attention, proves to be insufficient for certain graph problems. Through controlled experiments, the authors show that GATs struggle to fit training data effectively due to this limitation. To address this issue, they propose a solution in the form of GATv2 - a dynamic graph attention variant that offers enhanced expressiveness compared to traditional GATs. By modifying the order of operations within the architecture, GATv2 introduces dynamic attention capabilities that enable better adaptation to varying contexts within graphs. The authors conduct an extensive evaluation comparing GATv2 against GAT across multiple benchmarks including 11 OGB datasets. Remarkably, GATv2 consistently outperforms its predecessor while maintaining comparable parametric costs. The significance of this work lies in enhancing the capabilities of graph neural networks by introducing dynamic attention mechanisms through GATv2. This advancement not only addresses the limitations of static attention in GATs but also opens up new possibilities for more effective representation learning with graphs. Additionally, the availability of code for replication and integration into popular libraries such as PyTorch Geometric, Deep Graph Library and TensorFlow GNN library ensures widespread adoption and further research in this domain.