GreeDy and CoDy: Counterfactual Explainers for Dynamic Graphs

arXiv:2403.16846v1 Announce Type: new
Abstract: Temporal Graph Neural Networks (TGNNs), crucial for modeling dynamic graphs with time-varying interactions, face a significant challenge in explainability due to their complex model structure. Counterfactual explanations, crucial for understanding model decisions, examine how input graph changes affect outcomes. This paper introduces two novel counterfactual explanation methods for TGNNs: GreeDy (Greedy Explainer for Dynamic Graphs) and CoDy (Counterfactual Explainer for Dynamic Graphs). They treat explanations as a search problem, seeking input graph alterations that alter …

abstract arxiv challenge counterfactual cs.ai cs.lg decisions dynamic explainability face graph graph neural networks graphs interactions modeling networks neural networks novel paper temporal type understanding