Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark
Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a motif planted in the data. This protocol implicitly assumes that a GNN trained on such data relies on the intended motif. Although prior work has quest...
Steve Azzolin, Francesco Paolo Nerini, Stefano Teso et al.
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