提升图数据域适应性能，关键在于对齐源与目标图的同质性差异。
Homophily Enhanced Graph Domain Adaptation
- 利用混合滤波器平滑图信号，捕捉并缓解图间同质性差异。
- 实验表明，同质性差异会显著降低域适应性能，对齐后效果提升明显。
- 适用于标签稀缺场景下的跨图域知识迁移任务。
图域适应（GDA）旨在将已标注的源图知识迁移到未标注的目标图，以应对标签稀缺问题。本文指出，图同质性是图域对齐的关键因素，但现有方法长期忽视此点。分析显示，基准数据集中存在同质性差异；实证与理论均证明，此类差异会显著降低GDA性能，凸显同质性对齐的重要性。为此，我们提出一种新型同质性对齐算法，采用混合滤波器平滑图信号，有效捕捉并缓解源-目标图间的同质性差异。在多种基准上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. In this paper, we highlight the significance of graph homophily, a pivotal factor for graph domain alignment, which, however, has long been overlooked in existing approaches. Specifically, our analysis first reveals that homophily discrepancies exist in benchmarks. Moreover, we also show that homophily discrepancies degrade GDA performance from both empirical and theoretical aspects, which further underscores the importance of homophily alignment in GDA. Inspired by this finding, we propose a novel homophily alignment algorithm that employs mixed filters to smooth graph signals, thereby effectively capturing and mitigating homophily discrepancies between graphs. Experimental results on a variety of benchmarks verify the effectiveness of our method.
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