Framework GNN-AID: Graph Neural Network Analysis, Interpretation and Defense
Abstract
The rising demand for Trusted AI (TAI) underscores the need for interpretable and robust models, yet existing tools rarely support graph-structured data or integrate interpretability with security. At the same time, Graph Neural Networks (GNNs) deliver state-of-the-art performance on numerous graph tasks. We present GNN-AID (Graph Neural Network Analysis, Interpretation, and Defense), an open-source Python framework for analyzing, interpreting, and defending GNNs, addressing this critical gap. Built on PyTorch-Geometric, GNN-AID offers preloaded datasets, model libraries, flexible APIs, and a web interface for visualization and no-code model design. MLOps features further support reproducibility and experiment tracking.
Fields of Study & Subjects
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume
- 40
- Issue
- 48
- Pages
- 41634–41636
- Publication date
- 14 March 2026
- Language
- EN