Web: http://arxiv.org/abs/2209.07926

Sept. 19, 2022, 1:11 a.m. | Michele Guerra, Indro Spinelli, Simone Scardapane, Filippo Maria Bianchi

cs.LG updates on arXiv.org arxiv.org

Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been
introduced to enhance the expressive power of Graph Neural Networks (GNNs),
which was proved to be not higher than the 1-dimensional Weisfeiler-Leman
isomorphism test. The new paradigm suggests using subgraphs extracted from the
input graph to improve the model's expressiveness, but the additional
complexity exacerbates an already challenging problem in GNNs: explaining their
predictions. In this work, we adapt PGExplainer, one of the most recent
explainers for GNNs, to SGNNs. The proposed …

arxiv explainability graph graph neural networks networks neural networks

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