Graph-based methods, pivotal for label inference over interconnected objects
in many real-world applications, often encounter generalization challenges, if
the graph used for model training differs significantly from the graph used for
testing. This work delves into Graph Domain Adaptation (GDA) to address the
unique complexities of distribution shifts over graph data, where
interconnected data points experience shifts in features, labels, and in
particular, connecting patterns. We propose a novel, theoretically principled
method, Pairwise Alignment (Pair-Align) to counter graph structure shift by
mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align
uses edge weights to recalibrate the influence among neighboring nodes to
handle CSS and adjusts the classification loss with label weights to handle LS.
Our method demonstrates superior performance in real-world applications,
including node classification with region shift in social networks, and the
pileup mitigation task in particle colliding experiments. For the first
application, we also curate the largest dataset by far for GDA studies. Our
method shows strong performance in synthetic and other existing benchmark
datasets.

图领域适应性研究中，本文提出了一种新颖的、基于理论原则的方法，即配对校准（Pair-Align），以解决图结构的移动性问题。通过减轻条件结构移位和标签移位，Pair-Align 利用边权重重新校准相邻节点之间的影响，并通过标签权重调整分类损失来处理标签移位。本方法在实际应用中表现出卓越的性能，包括社交网络中具有区域移位的节点分类以及粒子碰撞实验中的堆积减灾任务。对于第一个应用，我们还创建了迄今为止最大的 GDA 研究数据集，并在合成和其他现有基准数据集上展示了强大的性能。

成对对齐提升图领域适应

Pairwise Alignment Improves Graph Domain Adaptation

In many real-world applications, graph-structured data used for training and
testing have differences in distribution, such as in high energy physics (HEP)
where simulation data used for training may not match real experiments. Graph
domain adaptation (GDA) is a method used to address these differences. However,
current GDA primarily works by aligning the distributions of node
representations output by a single graph neural network encoder shared across
the training and testing domains, which may often yield sub-optimal solutions.
This work examines different impacts of distribution shifts caused by either
graph structure or node attributes and identifies a new type of shift, named
conditional structure shift (CSS), which current GDA approaches are provably
sub-optimal to deal with. A novel approach, called structural reweighting
(StruRW), is proposed to address this issue and is tested on synthetic graphs,
four benchmark datasets, and a new application in HEP. StruRW has shown
significant performance improvement over the baselines in the settings with
large graph structure shifts, and reasonable performance improvement when node
attribute shift dominates.

本文研究图神经网络中的图域自适应问题，提出了一种新方法叫做结构重加权，用于解决现有方法无法适应的条件结构偏移问题，并在合成图、四个基准数据集和高能物理领域的一个新应用中进行了测试，表现出了显著的性能提升。