On the Benefits of Attribute-Driven Graph Domain Adaptation
This addresses a pressing challenge in cross-network learning for real-world graph datasets where labeled data is scarce, but it is incremental as it builds on existing GDA methods by focusing on node attributes.
The paper tackles the problem of Graph Domain Adaptation (GDA) by highlighting the overlooked role of node attributes, showing that attribute discrepancy is critical and often more substantial than structural shifts, and proposes a cross-channel module that achieves effective results on benchmarks.
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.