Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| author | Abbahaddou, Yassine Malliaros, Fragkiskos D. Lutzeyer, Johannes F. Aboussalah, Amine Mohamed Vazirgiannis, Michalis |
| author_facet | Abbahaddou, Yassine Malliaros, Fragkiskos D. Lutzeyer, Johannes F. Aboussalah, Amine Mohamed Vazirgiannis, Michalis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation Abbahaddou, Yassine Malliaros, Fragkiskos D. Lutzeyer, Johannes F. Aboussalah, Amine Mohamed Vazirgiannis, Michalis Machine Learning Social and Information Networks Applications 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. |
| title | Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation |
| topic | Machine Learning Social and Information Networks Applications |
| url | https://arxiv.org/abs/2411.08638 |