Cross-Network Relationship Learning for Node Classification via Optimal Transport

Cross-network node classification aims to transfer knowledge extracted from a source network with sufficient labeled nodes to predict labels for nodes in a target network. Existing methods mainly focus on associating two networks in a shared embedding space, where the distribution discrepancy of node embedding across two networks isminimized. However, the relationship across two networks remains underexplored. In this paper, we propose a method that learns cross-network relationship among nodes from different networks and performs information aggregation across networks based on our learned relationship. To achieve this, we introduce a labeled Fused Gromov-Wasserstein model based on optimal transport, which exploits feature, structure and label information to construct node association between two networks. Based on the cross-network relationship, we design a cross-network graph convolutional network to learn node embeddings. Experimental results on several benchmark datasets show the superiority of the proposed method over state-of-the-art methods.