Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

arXiv:2309.07402v2 Announce Type: replace
Abstract: Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be …

arxiv cs.lg domain domain adaptation entropy graphs minimax semi-supervised type