面向图域适应的跨分辨率语义学习
Cross-Resolution Semantic Learning for Graph Domain Adaptation
AI总结：
针对图域适应中语义分辨率偏移导致的负迁移问题，提出CReSL方法，通过多分辨率表示库、跨分辨率原型迁移与目标嫁接实现知识迁移，在多数图域适应设置下优于基线。
AI中文摘要：
图域适应（GDA）旨在分布偏移下将带标签源图的预测知识迁移至无标签目标图。现有方法通过对齐表示或正则化图结构实现迁移，但未显式建模不同源邻域范围学到的类别判别知识应如何跨目标范围路由。我们将图表示编码的邻域范围称为其传播分辨率，并定义语义分辨率偏移为类别判别证据最强的传播分辨率在跨域间的变化。此类偏移会使固定的同分辨率配对次优，并增加负迁移风险。为解决该问题，我们提出跨分辨率语义学习（CReSL），一种从跨域类别结构学习源-目标分辨率软对应关系的GDA方法。首先，CReSL利用共享图神经网络（GNN）和可学习分辨率嵌入构建多分辨率表示库，为每个源分辨率配备一个按分辨率索引的专家。其次，CReSL引入跨分辨率原型迁移，其从源标签和软目标后验构建类别-分辨率原型，并将跨域原型差异转换为目标分辨率上的专家特定路由。第三，CReSL引入跨目标分辨率嫁接，其构建后验加权的目标-源原型位移，并在类别不确定性下为实例级适应强制对应加权预测一致性。在多种域偏移下的图基准上开展的大量实验表明，CReSL在多数设置下优于强代表性基线。
英文摘要：
Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing methods align representations or regularize graph structures, but do not explicitly model how class-discriminative knowledge learned at different source neighborhood ranges should be routed across target ranges. We call the neighborhood range encoded by a graph representation its propagation resolution and define semantic resolution shift as a cross-domain change in the propagation resolutions at which class-discriminative evidence is strongest. Such shifts can make fixed same-resolution pairing suboptimal and increase the risk of negative transfer. To address this issue, we propose Cross-Resolution Semantic Learning (CReSL), a GDA method that learns soft sourceto-target resolution correspondence from cross-domain class structure. First, CReSL constructs a multi-resolution representation bank using a shared Graph Neural Network and learnable resolution embeddings, with a resolution-indexed expert for each source resolution. Second, CReSL introduces Cross-Resolution Prototype Transport, which constructs class-resolution prototypes from source labels and soft target posteriors and converts cross-domain prototype discrepancies into expert-specific routing over target resolutions. Third, CReSL introduces Cross-Resolution Target Grafting, which constructs posterior-weighted target-to-source prototype displacements and enforces correspondence-weighted prediction consistency for instance-level adaptation under class uncertainty. Extensive experiments on graph benchmarks under diverse domain shifts show that CReSL outperforms strong representative baselines across most settings.
