Leveraging Attribute Interaction and Self-Training for Graph Alignment via Optimal Transport

Unsupervised alignment of two attributed graphs finds the node correspondence between them without any known anchor links. The recently proposed optimal transport (OT)-based approaches tackle this problem via Gromov–Wasserstein distance and joint learning of graph structures and node attributes, which achieve better accuracy and stability compared to previous embedding-based methods. However, it remains largely unexplored under the OT framework to fully utilize both structure and attribute information. We propose an Optimal Transport-based Graph Alignment method with Attribute Interaction and Self-Training ( PORTRAIT ), with the following two contributions. First, we enable the interaction of different dimensions of node attributes in the Gromov–Wasserstein learning process, while simultaneously integrating multi-layer graph structural information and node embeddings into the design of the intra-graph cost, which yields more expressive power with theoretical guarantee. Second, the self-training strategy is integrated into the OT-based learning process to significantly enhance node alignment accuracy with the help of confident predictions. Extensive experimental results validate the efficacy of the proposed model.