Semi-supervised graph domain adaptation, as a subfield of graph transfer learning, seeks to
precisely annotate unlabeled target graph nodes by leveraging transferable features …

This paper addresses the challenge of incremental learning in growing graphs with
increasingly complex tasks. The goal is to continually train a graph model to handle new …

Graph domain adaptation has recently enabled knowledge transfer across different graphs.
However, without the semantic information on target graphs, the performance on target …

Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning,
particularly pertinent due to the absence of labeled data in real-world graph datasets …

Heterogeneous domain adaptation is a challenging problem in transfer learning since
samples from the source and target domains reside in different feature spaces with different …