ICML 2025

Graph Neural Network Generalization
with Gaussian Mixture Model Based Augmentation
Yassine Abbahaddou 1 * Fragkiskos D. Malliaros 2 Johannes F. Lutzeyer 1
Amine M. Aboussalah 3 Michalis Vazirgiannis 1 4
Abstract
Graph Neural Networks (GNNs) have shown
great promise in tasks like node and graph classi-
fication, but they often struggle to generalize, par-
ticularly to unseen or out-of-distribution (OOD)
data.
These challenges are exacerbated when
training data is limited in size or diversity. To
address these issues, we introduce a theoretical
framework using Rademacher complexity to com-
pute a regret bound on the generalization error
and then characterize the effect of data augmen-
tation. This framework informs the design of
GRATIN, an efficient graph data augmentation
algorithm leveraging the capability of Gaussian
Mixture Models (GMMs) to approximate any dis-
tribution. Our approach not only outperforms
existing augmentation techniques in terms of gen-
eralization but also offers improved time com-
plexity, making it highly suitable for real-world
applications. Our code is publicly available at:
https://github.com/abbahaddou/GRATIN.
1. Introduction
Graphs are a fundamental and ubiquitous structure for mod-
eling complex relationships and interactions. In biology,
graphs are employed to represent complex networks of pro-
tein interactions and in drug discovery by modeling molecu-
lar relationships (Gaudelet et al., 2021; Jagtap et al., 2022).
Similarly, social networks capture relationships and commu-
nity interactions (Aboussalah et al., 2023a; Zeng et al., 2022;
Malliaros & Vazirgiannis, 2013; Newman et al., 2002). To
address the unique challenges posed by graph-structured
*Work partially completed during a research visit to NYU.
1LIX, ´Ecole Polytechnique, IP Paris 2Universit´e Paris-Saclay,
CentraleSup´elec, Inria 3NYU Tandon School of Engineering
4MBZUAI. Correspondence to:
Yassine Abbahaddou <yas-
sine.abbahaddou@polytechnique.edu>.
Proceedings of the 42 nd...

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