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.

翻译：基于图的方法在许多实际应用中对于互联对象上的标签推断至关重要，但当用于模型训练的图与用于测试的图存在显著差异时，常会面临泛化挑战。本文深入研究了图域适应（Graph Domain Adaptation, GDA），以解决图数据上分布偏移的独特复杂性，其中互联数据点会在特征、标签，特别是连接模式上发生偏移。我们提出了一种新颖且具有理论依据的方法——成对对齐（Pairwise Alignment, Pair-Align），通过缓解条件结构偏移（Conditional Structure Shift, CSS）和标签偏移（Label Shift, LS）来应对图结构偏移。Pair-Align 利用边权重重新校准相邻节点间的影响以处理CSS，并通过标签权重调整分类损失以处理LS。我们的方法在实际应用中展现了卓越性能，包括社交网络中具有区域偏移的节点分类，以及粒子对撞实验中的堆积缓解任务。针对第一个应用，我们还整理了迄今为止用于GDA研究的最大数据集。我们的方法在合成数据集及其他现有基准数据集上均表现出强劲性能。