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

TL;DR

This paper introduces SemiGCL, a novel semi-supervised domain adaptation method for graphs that uses contrastive learning and minimax entropy to improve node classification across different graph domains.

Contribution

SemiGCL is the first approach to formally address semi-supervised domain adaptation on graphs using contrastive learning and adversarial entropy minimization.

Findings

SemiGCL outperforms state-of-the-art baselines on benchmark datasets.

Contrastive learning enhances node representation quality.

Adversarial entropy minimization reduces domain divergence.

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 formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph \textbf{Semi}-supervised domain adaptation with \textbf{G}raph \textbf{C}ontrastive \textbf{L}earning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph's local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. The source codes of SemiGCL are publicly available at https://github.com/ JiarenX/SemiGCL.