Graph Neural Networks (GNNs) have achieved great success on a variety of
tasks with graph-structural data, among which node classification is an
essential one. Unsupervised Graph Domain Adaptation (UGDA) shows its practical
value of reducing the labeling cost for node classification. It leverages
knowledge from a labeled graph (i.e., source domain) to tackle the same task on
another unlabeled graph (i.e., target domain). Most existing UGDA methods
heavily rely on the labeled graph in the source domain. They utilize labels
from the source domain as the supervision signal and are jointly trained on
both the source graph and the target graph. However, in some real-world
scenarios, the source graph is inaccessible because of either unavailability or
privacy issues. Therefore, we propose a novel scenario named Source Free
Unsupervised Graph Domain Adaptation (SFUGDA). In this scenario, the only
information we can leverage from the source domain is the well-trained source
model, without any exposure to the source graph and its labels. As a result,
existing UGDA methods are not feasible anymore. To address the non-trivial
adaptation challenges in this practical scenario, we propose a model-agnostic
algorithm for domain adaptation to fully exploit the discriminative ability of
the source model while preserving the consistency of structural proximity on
the target graph. We prove the effectiveness of the proposed algorithm both
theoretically and empirically. The experimental results on four cross-domain
tasks show consistent improvements of the Macro-F1 score up to 0.17.

本研究提出了一个基于无监督领域自适应的节点分类方法，该方法利用了已经训练好的源模型，而不需要访问源图和其标签，实现了对无标签目标图的分类，实验结果表明该方法在四项跨领域任务中都获得了显著的改进。