同质性增强的图域自适应
Homophily Enhanced Graph Domain Adaptation
- Western University（西安大略大学）
- Michigan State University（密歇根州立大学）
- Vector Institute（Vector研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对图域自适应中标签稀缺问题，揭示同质性差异对性能的负面影响，并提出基于混合滤波器的同质性对齐算法，有效提升跨图迁移效果。
AI中文摘要：
图域自适应（GDA）将知识从带标签的源图迁移到无标签的目标图，以应对标签稀缺的挑战。本文强调了图同质性的重要性，这是图域对齐的一个关键因素，然而在现有方法中长期以来一直被忽视。具体而言，我们的分析首先揭示了基准数据集中存在同质性差异。此外，我们还从实证和理论两个方面表明，同质性差异会降低GDA的性能，这进一步凸显了在GDA中进行同质性对齐的重要性。受这一发现的启发，我们提出了一种新颖的同质性对齐算法，该算法采用混合滤波器来平滑图信号，从而有效捕获并缓解图之间的同质性差异。在多种基准数据集上的实验结果验证了我们方法的有效性。
英文摘要：
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.
