ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

arXiv:2403.09171v1 Announce Type: new
Abstract: Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention. Among prior GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness …

abstract adversarial arxiv augmentation cs.ai cs.lg data edge gather gnns graph graph data graph neural networks information networks neural networks nodes performance robust robustness solution type via