Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
This addresses generalization issues in GNNs for tasks like node and graph classification, which is an incremental improvement over existing augmentation methods.
The paper tackled the problem of Graph Neural Networks (GNNs) struggling to generalize to unseen or out-of-distribution data, especially with limited training data, by introducing GRATIN, a graph data augmentation algorithm based on Gaussian Mixture Models, which outperforms existing techniques in generalization and offers improved time complexity.
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.