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

Subgraph isomorphism counting is an important problem on graphs, as many
graph-based tasks exploit recurring subgraph patterns. Classical methods
usually boil down to a backtracking framework that needs to navigate a huge
search space with prohibitive computational costs. Some recent studies resort
to graph neural networks (GNNs) to learn a low-dimensional representation for
both the query and input graphs, in order to predict the number of subgraph
isomorphisms on the input graph. However, typical GNNs employ a node-centric
message passing scheme that receives and aggregates messages on nodes, which is
inadequate in complex structure matching for isomorphism counting. Moreover, on
an input graph, the space of possible query graphs is enormous, and different
parts of the input graph will be triggered to match different queries. Thus,
expecting a fixed representation of the input graph to match diversely
structured query graphs is unrealistic. In this paper, we propose a novel GNN
called Count-GNN for subgraph isomorphism counting, to deal with the above
challenges. At the edge level, given that an edge is an atomic unit of encoding
graph structures, we propose an edge-centric message passing scheme, where
messages on edges are propagated and aggregated based on the edge adjacency to
preserve fine-grained structural information. At the graph level, we modulate
the input graph representation conditioned on the query, so that the input
graph can be adapted to each query individually to improve their matching.
Finally, we conduct extensive experiments on a number of benchmark datasets to
demonstrate the superior performance of Count-GNN.

本研究提出了一种名为 Count-GNN 的新型图神经网络模型，采用边为中心的消息传递方案和在图级别上进行的表示修正来解决子图同构计数问题，并在多个基准数据集上进行了广泛的实验证明其优越性能。