  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.
