Message-passing graph neural networks (MPNNs) have emerged as the leading approach for machine learning on graphs, attracting significant attention in recent years. While a large set of works explored the expressivity of MPNNs, i.e., their ability to separate graphs and approximate functions over them, comparatively less attention has been directed toward investigating their generalization abilities, i.e., making meaningful predictions beyond the training data. Here, we systematically review the existing literature on the generalization abilities of MPNNs. We analyze the strengths and limitations of various studies in these domains, providing insights into their methodologies and findings. Furthermore, we identify potential avenues for future research, aiming to deepen our understanding of the generalization abilities of MPNNs.
View on arXiv@article{vasileiou2025_2503.15650,
title={ Survey on Generalization Theory for Graph Neural Networks },
author={ Antonis Vasileiou and Stefanie Jegelka and Ron Levie and Christopher Morris },
journal={arXiv preprint arXiv:2503.15650},
year={ 2025 }
}