  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 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 called SOGA 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 in the Macro-F1 score and Macro-AUC.
