AI 中文总结
针对图域适应仅依赖图表示导致证据不足的问题，提出EviGDA框架，融合图感知与图无关专家，在十个数据集上超越现有方法。
AI 中文摘要
图域适应（GDA）在节点属性和图结构均发生偏移的情况下，将知识从有标签的源图迁移到无标签的目标图。现有方法主要通过传播重设计、分布对齐或源到目标转换建模来调整图表示，但仍依赖单一图传播路径进行目标预测。这留下了一个问题：调整后的图表示是否穷尽了目标域中可用的预测证据，因为图感知专家和图无关的局部专家在拓扑偏移下可能表现出不同的失败模式。为解决这一局限，我们提出EviGDA，一个证据增强的图域适应框架，用图无关的局部专家补充图表示适应。图感知专家执行消息传递和熵感知边际对齐，而图无关的局部专家仅从源节点特征和标签学习，不进行图传播或目标对齐。两个专家独立优化，仅在推理时通过任务级常数概率混合组合，保留互补证据，无需联合训练、学习路由或目标伪标签。在十个数据集和16个迁移任务上的大量实验表明，EviGDA优于最先进的基线。
英文摘要
Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and graph structure. Existing methods primarily adapt graph representations through propagation redesign, distribution alignment, or source-to-target transition modeling, but still rely on a single graph-propagating path for target prediction. This leaves open whether an adapted graph representation exhausts the predictive evidence available in the target domain, since the graph-aware expert and graph-free local expert may exhibit different failure modes under topological shifts. To address this limitation, we propose EviGDA, an Evidence-Augmented Graph Domain Adaptation framework that complements graph representation adaptation with a graph-free local expert. The graph-aware expert performs message passing and entropy-aware marginal alignment, while the graph-free local expert learns solely from source node features and labels without graph propagation or target alignment. The two experts are optimized independently and combined only at inference through a task-level constant probability mixture, preserving complementary evidence without joint training, learned routing, or target pseudo-labels. Extensive experiments on ten datasets and 16 transfer tasks show that EviGDA outperforms state-of-the-art baselines. Complementary prediction paths add value beyond graph representation alignment.
