Graph domain adaptation (GDA) aims to address the challenge of limited label data in the
target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for …

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 …

Graph domain adaptation, which falls under the umbrella of graph transfer learning, involves
transferring knowledge from a labeled source graph to improve prediction accuracy on an …

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 …

Abstract Domain adaptation has become an attractive learning paradigm, as it can leverage
source domains with rich labels to deal with classification tasks in an unlabeled target …

Recent years have witnessed tremendous interest in deep learning on graph-structured
data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is …

As graph representation learning often suffers from label scarcity problems in real-world
applications, researchers have proposed graph domain adaptation (GDA) as an effective …

Graph-based methods, pivotal for label inference over interconnected objects in many real-
world applications, often encounter generalization challenges, if the graph used for model …

Graph domain adaptation models are widely adopted in cross-network learning tasks to
transfer labeling or structural knowledge. Currently, there mainly exist two limitations in …

As a specific case of graph transfer learning, unsupervised domain adaptation on graphs
aims for knowledge transfer from label-rich source graphs to unlabeled target graphs …