Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
Abbahaddou, Yassine Malliaros, Fragkiskos D. Lutzeyer, Johannes F. Aboussalah, Amine Mohamed Vazirgiannis, Michalis
Abbahaddou, Yassine
Malliaros, Fragkiskos D.
Lutzeyer, Johannes F.
Aboussalah, Amine Mohamed
Vazirgiannis, Michalis
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Department
Machine Learning
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly 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 compute a regret bound on the generalization error and then characterize the effect of data augmentation. This framework informs the design of GRATIN, an efficient graph data augmentation algorithm leveraging the capability of Gaussian Mixture Models (GMMs) to approximate any distribution. Our approach not only outperforms existing augmentation techniques in terms of generalization but also offers improved time complexity, making it highly suitable for real-world applications. Our code is publicly available at: https://github.com/abbahaddou/GRATIN.
Citation
Y. Abbahaddou, F. D. Malliaros, J. F. Lutzeyer, A. M. Aboussalah, and M. Vazirgiannis, “Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/abbahaddou25a.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
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Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press