Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical
solution to transfer knowledge from a label-rich source graph to a completely
unlabelled target graph. However, most methods require a labelled source graph
to provide supervision signals, which might not be accessible in the real-world
settings due to regulations and privacy concerns. In this paper, we explore the
scenario of source-free unsupervised graph domain adaptation, which tries to
address the domain adaptation problem without accessing the labelled source
graph. Specifically, we present a novel paradigm called GraphCTA, which
performs model adaptation and graph adaptation collaboratively through a series
of procedures: (1) conduct model adaptation based on node's neighborhood
predictions in target graph considering both local and global information; (2)
perform graph adaptation by updating graph structure and node attributes via
neighborhood contrastive learning; and (3) the updated graph serves as an input
to facilitate the subsequent iteration of model adaptation, thereby
establishing a collaborative loop between model adaptation and graph
adaptation. Comprehensive experiments are conducted on various public datasets.
The experimental results demonstrate that our proposed model outperforms recent
source-free baselines by large margins.

我们提出了一种名为 GraphCTA 的新范式，通过一系列步骤：在考虑本地和全局信息的情况下，基于目标图中节点邻居的预测进行模型适应；通过邻域对比学习来更新图结构和节点属性进行图适应；以及使用更新后的图作为输入来促进后续模型适应的迭代，从而在没有访问标记源图的情况下解决领域适应问题。我们在各种公共数据集上进行了全面的实验，实验结果表明我们提出的模型较最近的无源基准模型有很大的改进。

合作自适应：无源图领域自适应的双向适应

Collaborate to Adapt: Source-Free Graph Domain Adaptation via  Bi-directional Adaptation

Unsupervised Graph Domain Adaptation (UGDA) aims to transfer knowledge from a
labelled source graph to an unlabelled target graph in order to address the
distribution shifts between graph domains. Previous works have primarily
focused on aligning data from the source and target graph in the representation
space learned by graph neural networks (GNNs). However, the inherent
generalization capability of GNNs has been largely overlooked. Motivated by our
empirical analysis, we reevaluate the role of GNNs in graph domain adaptation
and uncover the pivotal role of the propagation process in GNNs for adapting to
different graph domains. We provide a comprehensive theoretical analysis of
UGDA and derive a generalization bound for multi-layer GNNs. By formulating GNN
Lipschitz for k-layer GNNs, we show that the target risk bound can be tighter
by removing propagation layers in source graph and stacking multiple
propagation layers in target graph. Based on the empirical and theoretical
analysis mentioned above, we propose a simple yet effective approach called
A2GNN for graph domain adaptation. Through extensive experiments on real-world
datasets, we demonstrate the effectiveness of our proposed A2GNN framework.

无监督图域自适应旨在将标记的源图知识转移至未标记的目标图，以解决图域之间的分布偏移。作者重新评估了图域自适应中图神经网络的作用，并发现了传播过程在适应不同图域中的关键作用。基于经验和理论分析，作者提出了名为 A2GNN 的简单而有效的图域自适应方法。通过对真实数据集的广泛实验，作者证明了所提出的 A2GNN 框架的有效性。

无监督图领域自适应中的传播再思考

Rethinking Propagation for Unsupervised Graph Domain Adaptation

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。