Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. In particular, to overcome label scarcity, we employ the spectral seriation algorithm to infer the robust pairwise rankings, which can guide semantic learning using a similarity learning objective. To depict distribution shifts, we utilize spectral clustering and the silhouette coefficient to detect harmonic graphs, which the source model can easily classify. To reduce potential domain discrepancy, we extract domain-invariant subgraphs from inharmonic graphs by an adversarial edge sampling process, which guides the invariant learning of GNNs. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA.

翻译：图神经网络（GNNs）在图域适应任务中已取得显著性能。然而，由于隐私与存储限制，现实场景中通常无法获取大量源图数据。为此，我们研究了一个尚未充分探索但具有实际意义的无源图域适应问题，该问题通过迁移源模型（而非源图）的知识至目标域。为解决此问题，我们提出了一种基于GNN的新方法——排序与对齐（RNA），该方法通过谱序列化对图相似度进行排序以实现鲁棒语义学习，并通过将非和谐图与接近源域的和谐图对齐以进行子图提取。具体而言，为克服标签稀缺性，我们采用谱序列化算法推断鲁棒的成对排序，该排序可通过相似度学习目标指导语义学习。为刻画分布偏移，我们利用谱聚类与轮廓系数检测源模型易于分类的和谐图。为减小潜在域差异，我们通过对抗性边采样过程从非和谐图中提取域不变子图，从而指导GNN的不变学习。在多个基准数据集上的大量实验证明了我们提出的RNA方法的有效性。