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.

本文探讨了基于图注意网络的 GATv2 在小型稀疏图结构中可能遇到的一些优化问题，并提出了改进的解决方案和方法，通过实验结果验证改进后的模型方案在节点级别回归任务中表现更好，同时提高了参数初始化的鲁棒性，并对其解释性进行了分析，以此来深入了解注意力机制。

面向小型稀疏图结构的图注意力网络在汽车应用中的优化与解释

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

This work provides a comprehensive derivation of the parameter gradients for
GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
GATs have proven to be powerful frameworks for processing graph-structured data
and, hence, have been used in a range of applications. However, the achieved
performance by these attempts has been found to be inconsistent across
different datasets and the reasons for this remains an open research question.
As the gradient flow provides valuable insights into the training dynamics of
statistically learning models, this work obtains the gradients for the
trainable model parameters of GATv2. The gradient derivations supplement the
efforts of [2], where potential pitfalls of GATv2 are investigated.

本文提供了 Graph Attention Networks 中 GATv2 的参数梯度全面推导，获取了可训练模型参数的梯度用于训练动态的统计学习模型，从而帮助解决 GATv2 不同数据集表现不一之问题。

图注意力网络中可学习参数的梯度推导

Gradient Derivation for Learnable Parameters in Graph Attention Networks

Graph Attention Networks (GATs) are one of the most popular GNN architectures
and are considered as the state-of-the-art architecture for representation
learning with graphs. In GAT, every node attends to its neighbors given its own
representation as the query. However, in this paper we show that GAT computes a
very limited kind of attention: the ranking of the attention scores is
unconditioned on the query node. We formally define this restricted kind of
attention as static attention and distinguish it from a strictly more
expressive dynamic attention. Because GATs use a static attention mechanism,
there are simple graph problems that GAT cannot express: in a controlled
problem, we show that static attention hinders GAT from even fitting the
training data. To remove this limitation, we introduce a simple fix by
modifying the order of operations and propose GATv2: a dynamic graph attention
variant that is strictly more expressive than GAT. We perform an extensive
evaluation and show that GATv2 outperforms GAT across 11 OGB and other
benchmarks while we match their parametric costs. Our code is available at
this https URL . GATv2 is available as part
of the PyTorch Geometric library, the Deep Graph Library, and the TensorFlow
GNN library.

该研究介绍了 Graph Attention Networks (GATs) 架构，它们被认为是最先进的图形表示学习架构之一，但存在限制性静态注意力机制，限制了它们在表达简单图形问题方面的能力；因此，提出了一种动态图形注意力变量 GATv2，相对于 GAT 更具表现力。