Collaborative graph neural networks for augmented graphs: A local-to-global perspective

Qihang Guo, Xibei Yang, Ming Li, Yuhua Qian

Abstract: Highlights•A novel embedding fusion architecture for augmented graphs is proposed.•A novel perturbation augmentation strategy is developed.•A novel scheme of pairwise graph combination is proposed.•A novel global loss function is developed.