Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network branch and a hierarchical graph kernel network branch, which explore graph topology in implicit and explicit manners. Besides, we incorporate coupled branches into a holistic multi-view contrastive learning framework, which not only incorporates graph representations learned from complementary views for enhanced understanding, but also encourages the similarity between cross-domain example pairs with the same semantics for domain alignment. Extensive experiments on popular datasets show that our CoCo outperforms these competing baselines in different settings generally.

翻译：尽管图神经网络（GNN）在图分类任务中取得了显著成就，但它们通常需要大量特定任务的标签，而获取这些标签的成本可能极高。一种可靠的解决方案是探索额外的带标签图数据，以增强目标域上的无监督学习。然而，由于对图拓扑结构挖掘不足以及显著的域间差异，如何将GNN应用于域自适应问题仍未得到解决。本文提出耦合对比图表示学习框架（CoCo），该框架通过耦合的学习分支提取拓扑信息，并利用耦合对比学习减少域间差异。CoCo包含一个图卷积网络分支和一个分层图核网络分支，分别以隐式和显式方式探索图拓扑。此外，我们将耦合分支整合到一个整体的多视图对比学习框架中，该框架不仅融合了从互补视图学习到的图表示以增强理解，还通过鼓励具有相同语义的跨域样本对之间的相似性来实现域对齐。在多个主流数据集上的大量实验表明，我们的CoCo方法在不同设置下普遍优于现有基线模型。