On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

Understand

The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge.

• A convincing explanation should be both necessary and sufficient simultaneously.
• However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two.
• Theoretically, the Probability of Necessity and Sufficiency (PNS) holds the potential to identify the most necessary and sufficient explanation since it can mathematically quantify the necessity and sufficiency of an explanation.