Graph Attention Networks (GATs) are designed to provide flexible neighborhood
aggregation that assigns weights to neighbors according to their importance. In
practice, however, GATs are often unable to switch off task-irrelevant
neighborhood aggregation, as we show experimentally and analytically. To
address this challenge, we propose GATE, a GAT extension that holds three major
advantages: i) It alleviates over-smoothing by addressing its root cause of
unnecessary neighborhood aggregation. ii) Similarly to perceptrons, it benefits
from higher depth as it can still utilize additional layers for (non-)linear
feature transformations in case of (nearly) switched-off neighborhood
aggregation. iii) By down-weighting connections to unrelated neighbors, it
often outperforms GATs on real-world heterophilic datasets. To further validate
our claims, we construct a synthetic test bed to analyze a model's ability to
utilize the appropriate amount of neighborhood aggregation, which could be of
independent interest.

GATE 是一种 GAT 的扩展，通过解决不必要的邻居聚合的根本原因，减轻了过平滑问题，并通过减小与不相关邻居的连接权重，在真实世界的异质数据集上常常优于 GAT。

GATE: 如何阻止侵入的邻居

GATE: How to Keep Out Intrusive Neighbors

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。