PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks

arXiv:2210.17159v2 Announce Type: replace
Abstract: Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored to a given graph instance. In our study, we propose Prototype-bAsed GNN-Explainer (PAGE), a novel model-level GNN explanation method that explains what the underlying GNN model has learned …

abstract arxiv attention cs.ai cs.it cs.lg cs.ne cs.si demand framework gnn gnns graph graph neural networks graph representation instance math.it networks neural networks page representation representation learning studies type