A True-to-the-Model Benchmark for Edge-Level Attributions of GNN Explainers

Francesco Paolo Nerini, Francesco Bonchi, André Panisson

Abstract: Edge-level explainers for Graph Neural Networks (GNNs) aim to identify the most crucial edges that influence the model’s predictions in a node classification task. Benchmarking these explainers is particularly challenging due to the extensive search space of potential explanations and the absence of reliable ground truths for edge importance. Moreover, the evaluation methods which are prominent in the literature rely on assumptions about which subgraphs in the input data influence the classification of a node, yet they provide no guarantee that the model has effectively learned the intended behavior.

External IDs:dblp:conf/pkdd/NeriniBP25