Global Counterfactual Explainer for Graph Neural Networks. (arXiv:2210.11695v2 [cs.LG] UPDATED)

Graph neural networks (GNNs) find applications in various domains such as
computational biology, natural language processing, and computer security.
Owing to their popularity, there is an increasing need to explain GNN
predictions since GNNs are black-box machine learning models. One way to
address this is counterfactual reasoning where the objective is to change the
GNN prediction by minimal changes in the input graph. Existing methods for
counterfactual explanation of GNNs are limited to instance-specific local
reasoning. This approach has two …

arxiv explainer global graph graph neural networks networks neural networks