忽略节点属性会削弱图域自适应效果，本文提出新方法融合并对齐属性与结构信息。
On the Benefits of Attribute-Driven Graph Domain Adaptation
- 从理论上证明节点属性差异是图域自适应的关键影响因素
- 实验证明属性偏移比拓扑偏移更显著，凸显属性对齐的重要性
- 设计跨通道模块，同时对齐源与目标图的属性与结构特征
图域自适应（GDA）解决跨网络学习中标签数据缺失的难题。现有方法多关注消除图结构差异，但忽略了节点属性的关键作用。本文首次从理论上证明：除图结构差异外，节点属性差异同样严重影响GDA性能。实证表明，属性偏移程度超过拓扑偏移，进一步强调了属性对齐的必要性。受此启发，提出一种新颖的跨通道模块，用于融合并对齐源图与目标图的属性与结构视图。在多个基准数据集上的实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscores the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module is developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmarks verify the effectiveness of our method.
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