Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity

Understand

Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design.

• However, GNNs lack transparency as they cannot provide understandable explanations for their predictions.
• To address this issue, counterfactual reasoning is used.
• The main goal is to make minimal changes to the input graph of a GNN in order to alter its prediction.