DEAL: An Unsupervised Domain Adaptive Framework for Graph-level Classification

Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification tasks. They have been primarily studied in cases of supervised end-to-end training, which requires abundant task-specific labels. Unfortunately, annotating labels of graph data could be prohibitively expensive or even impossible in many applications. An effective solution is to incorporate labeled graphs from a different, but related source domain, to develop a graph classification model for the target domain. However, the problem of unsupervised domain adaptation for graph classification is challenging due to potential domain discrepancy in graph space as well as the label scarcity in the target domain. In this paper, we present a novel GNN framework named DEAL by incorporating both source graphs and target graphs, which is featured by two modules, i.e., adversarial perturbation and pseudo-label distilling. Specifically, to overcome domain discrepancy, we equip source graphs with target semantics by applying to them adaptive perturbations which are adversarially trained against a domain discriminator. Additionally, DEAL explores distinct feature spaces at different layers of the GNN encoder, which emphasize global and local semantics respectively. Then, we distill the consistent predictions from two spaces to generate reliable pseudo-labels for sufficiently utilizing unlabeled data, which further improves the performance of graph classification. Extensive experiments on a wide range of graph classification datasets reveal the effectiveness of our proposed DEAL.