Post #373

Generalization Bounds of GNN Expressiveness, that is what class of graphs can be represented by GNN, has been extensively studied during the last two years. On the other hand, generalization, i.e. ability to represent correctly unseen graphs is just gaining attention. Here are some papers that study generalization of GNN. - Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NeurIPS 2020 - Generalization and Representational Limits of Graph Neural Networks ICML 2020 - Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020 - A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Arxiv Dec 2020