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Found 3 papers, 2 papers with code
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques.
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design.
Task and Model Agnostic Adversarial Attack on Graph Neural Networks
In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.
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