Papers › Covered Forest: Fine-grained generalization analysis of graph neural networks

Covered Forest: Fine-grained generalization analysis of graph neural networks

10 Dec 2024arXiv:2412.07106archive 2025-07-28

Antonis Vasileiou, Ben Finkelshtein, Floris Geerts, Ron Levie, Christopher Morris

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.

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