Covered Forest: Fine-grained generalization analysis of graph neural networks
Antonis Vasileiou, Ben Finkelshtein, Floris Geerts, Ron Levie, Christopher Morris
Abstract
The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. However, MPNNs' generalization abilities---making meaningful predictions beyond the training set---remain less explored. Current generalization analyses often overlook graph structure, limit the focus to specific aggregation functions, and assume the impractical, hard-to-optimize $0$-$1$ loss function. Here, we extend recent advances in graph similarity theory to assess the influence of graph structure, aggregation, and loss functions on MPNNs' generalization abilities. Our empirical study supports our theoretical insights, improving our understanding of MPNNs' generalization properties.
BibTeX
@inproceedings{
vasileiou2025covered,
title={Covered Forest: Fine-grained generalization analysis of graph neural networks},
author={Antonis Vasileiou and Ben Finkelshtein and Floris Geerts and Ron Levie and Christopher Morris},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=xvLVYrYQ8a}
}