As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. However, graphs with topology and attributes usually have considerable cross-domain disparity and there are numerous real-world scenarios where merely a subset of nodes are labeled in the source graph. This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes. Moreover, to address the label scarcity, we propose pseudo-labeling on unlabeled nodes, which improves classification on the target graph via measuring the posterior influence of nodes based on their relative position to the class centroids. Finally, extensive experiments on a range of publicly accessible datasets validate the effectiveness of our proposed SGDA in different experimental settings.

翻译：作为图迁移学习的一种特定情况，无监督域适应旨在将知识从标签丰富的源图迁移到无标签的目标图。然而，具有拓扑结构和属性的图通常存在显著的跨域差异，并且在实际场景中，源图中仅有部分节点带有标签。由于严重的域偏移和标签稀缺，这给图迁移学习带来了关键挑战。为解决这些问题，我们提出了一种名为半监督图域适应（SGDA）的方法。针对域偏移，我们为每个源节点添加自适应偏移参数，并通过对抗训练方式对齐节点嵌入的跨域分布，从而使在带标签源节点上训练的分类器能够迁移至目标节点。此外，为解决标签稀缺问题，我们提出对未标记节点进行伪标签标注，通过基于节点相对于类别中心的相对位置后验影响度量，提升目标图的分类性能。最后，在多个公开数据集上进行的广泛实验验证了所提出的SGDA在不同实验场景下的有效性。