@inproceedings{
hajiramezanali2023on,
title={On the Consistency of {GNN} Explainability Methods},
author={Ehsan Hajiramezanali and Sepideh Maleki and Alex Tseng and Aicha BenTaieb and Gabriele Scalia and Tommaso Biancalani},
booktitle={NeurIPS 2023 Workshop: New Frontiers in Graph Learning},
year={2023},
url={https://openreview.net/forum?id=4tGqks76l7}
}
Despite the widespread utilization of post-hoc explanation methods for graph neural networks (GNNs) in high-stakes settings, there has been a lack of comprehensive evaluation regarding their quality and reliability. This evaluation is challenging primarily due to non-Euclidean nature of the data, arbitrary size, and complex topological structure. In this context, we argue that the \emph{consistency} of GNN explanations, denoting the ability to produce similar explanations for input graphs with minor structural changes that do not alter their output predictions, is a key requirement for effective post-hoc GNN explanations. To fulfill this gap, we introduce a novel metric based on Fused Gromov-Wasserstein distance to quantify consistency.
On the Consistency of GNN Explainability Methods
Ehsan Hajiramezanali Sepideh Maleki Alex Tseng Aicha BenTaieb Gabriele Scalia Tommaso Biancalani