Abstract: Aiming at the challenge of adaptive graph learning in unsupervised domains, we proposed a domain adaptive graph convolutional network based on data augmentation. The network first constructed a high-order neighborhood relationship matrix and adjacency matrix to jointly guide information propagation and learn more comprehensive graph node representations, and then contrastive learning based on data augmentation was introduced for graph domain alignment, which not only extracted semantic information within a single domain but also promoted more sufficient knowledge transfer between domains. Experimental evaluation results  on Citation network datasets show  that the proposed method can transfer rich labeled knowledge from the source graph domain to the unlabeled target graph domain, solve the reliance on labels for graph representation learning, reduce the cost of  manual annotation, and outperform the  graph domain adaptation classical algorithms.

Key words: graph domain adaptation, node representation, data enhancement, graph convolutional network