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

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. To tackle the SSDA problem on graphs, a novel method called SemiGCL is proposed, which benefits from graph contrastive learning 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.

Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs

Graph embedding is a general approach to tackling graph-analytic problems by encoding nodes into low-dimensional representations. Most existing embedding methods are transductive since the information of all nodes is required in training, including those to be predicted. In this paper, we propose a novel inductive embedding method for semi-supervised learning on graphs. This method generates node representations by learning a parametric function to aggregate information from the neighborhood using an attention mechanism, and hence naturally generalizes to previously unseen nodes. Furthermore, adversarial training serves as an external regularization enforcing the learned representations to match a prior distribution for improving robustness and generalization ability. Experiments on real-world clean or noisy graphs are used to demonstrate the effectiveness of this approach.