IC-1045Factual GNN explanations do not capture the full data signal: retraining on explanations fails to reproduce predictions while retraining on residuals preserves them
The paper measures two reproducibility metrics. Reproducibility+ retrains a GNN on only the explanation subgraphs and compares test accuracy to the original model; values are consistently below 1.0, meaning the explanations alone are insufficient. Reproducibility- retrains a GNN on the residual graphs (original minus explanation) and finds accuracy remains close to the original, meaning the explanation edges are not the true cause of the prediction. The paper concludes that explainers capture only specific signals learned by the GNN and do not encompass all underlying data signals.
Evidence
correlational
Key metric
Reproducibility- stays near 1.0 across all methods and datasets (e.g. mutagenicity 0.99-1.06, AIDS 0.99-1.0, NCI1 0.89-0.93 in Table U); Reproducibility+ below 1.0 for most methods (e.g. mutagenicity 0.97-1.11, MUTAG 0.11-1.08 in Table T)
Caveat
SubgraphX results are unreliable due to very high variance from using only a small number of test graphs (stated in Table T and U captions). The base GNNs are small (3 layers, 20 hidden dims) and trained by the authors.