  As one of popular quantitative metrics to assess the quality of explanation
of graph neural networks (GNNs), fidelity measures the output difference after
removing unimportant parts of the input graph. Fidelity has been widely used
due to its straightforward interpretation that the underlying model should
produce similar predictions when features deemed unimportant from the
explanation are removed. This raises a natural question: "Does fidelity induce
a global (soft) mask for graph pruning?" To solve this, we aim to explore the
potential of the fidelity measure to be used for graph pruning, eventually
enhancing the GNN models for better efficiency. To this end, we propose
Fidelity$^-$-inspired Pruning (FiP), an effective framework to construct global
edge masks from local explanations. Our empirical observations using 7 edge
attribution methods demonstrate that, surprisingly, general eXplainable AI
methods outperform methods tailored to GNNs in terms of graph pruning
performance.
