Graph Domain Adaptation Does Not End with Representation Learning

2026-09-22

AI Summary

为解决图域适应中仅依赖单一传播路径的问题，提出EviGDA框架，结合图感知专家和无图局部专家以增强预测准确性。

Abstract

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.

Problem

• Graph Domain Adaptation
• Representation Learning
• Topological Shifts

Innovation

• Graph Domain Adaptation
• Evidence-Augmented
• Graph-Free Local Expert
• Entropy-Aware Marginal Alignment
• Task-Level Constant Probability Mixture