Domain Adaptive Graph Infomax via Conditional Adversarial Networks

Abstract

The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks. However, existing GNNs are mainly devised to classify nodes in a (partially) labeled graph. To classify nodes in a newly-collected unlabeled graph, it is desirable to transfer label information from an existing labeled graph. To address this cross-graph node classification problem, we propose a graph infomax method that is domain adaptive. Node representations are computed through neighborhood aggregation. Mutual information is maximized between node representations and global summaries, encouraging node representations to encode the global structural information. Conditional adversarial networks are employed to reduce the domain discrepancy by aligning the multimodal distributions of node representations. Experimental results in real-world datasets validate the performance of our method in comparison with the state-of-the-art baselines.