Framework GNN-AID: Graph Neural Network Analysis, Interpretation and Defense
Kirill Lukianov, Mikhail Drobyshevskiy, Georgii Sazonov, Mikhail Soloviov, Ilya Makarov
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.
BibTeX
@inproceedings{aaai2026_frameworkgnnaidg,
title = {Framework GNN-AID: Graph Neural Network Analysis, Interpretation and Defense},
author = {Kirill Lukianov and Mikhail Drobyshevskiy and Georgii Sazonov and Mikhail Soloviov and Ilya Makarov},
booktitle = {AAAI 2026},
year = {2026}
}