# Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications

arXiv: https://arxiv.org/abs/2305.16196 · HTML: https://mlsanity.com/paper/2305.16196/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Understanding Attention and Generalization in Graph Neural Networks](https://mlsanity.com/paper/1905.02850/) (2019)
- [PlanT: Explainable Planning Transformers via Object-Level Representations](https://mlsanity.com/paper/2210.14222/) (2022)
- [How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision](https://mlsanity.com/paper/2204.04879/) (2022)
- [Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks](https://mlsanity.com/paper/2003.08414/) (2020)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers](https://mlsanity.com/paper/2211.03495/) (2022)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2305.07416/) (2023)
- [Attentive Explanations: Justifying Decisions and Pointing to the Evidence](https://mlsanity.com/paper/1612.04757/) (2016)
- [Attentive cross-modal paratope prediction](https://mlsanity.com/paper/1806.04398/) (2018)
- [Universal Function Approximation on Graphs](https://mlsanity.com/paper/2003.06706/) (2020)
- [Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)](https://mlsanity.com/paper/1711.07373/) (2017)

## Built on by (most cited first, 3 of 3)

- [Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models](https://mlsanity.com/paper/2405.14384/) (2024)
- [Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain](https://mlsanity.com/paper/2309.16702/) (2023)
- [Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis](https://mlsanity.com/paper/2506.02599/) (2025)

## Abstract (arXiv)

For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and sparse graph structures a proper optimization is problematic. To surpass limitations, this work proposes architectural modifications of GATv2. In controlled experiments, it is shown that the proposed model adaptions improve prediction performance in a node-level regression task and make it more robust to parameter initialization. This work aims for a better understanding of the attention mechanism and analyzes its interpretability of identifying causal importance.