Contrastive Graph Neural Network Explanation

• URL: http://arxiv.org/abs/2010.13663v1
• Date: Mon, 26 Oct 2020 15:32:42 GMT
• Title: Contrastive Graph Neural Network Explanation
• Authors: Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer
• Abstract summary: Graph Neural Networks achieve remarkable results on problems with structured data but come as black-box predictors. We argue that explicability must use graphs compliant with the distribution underlying the training data. We present a novel Contrastive GNN Explanation technique following this paradigm.
• Score: 13.234975857626749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks achieve remarkable results on problems with structured data but come as black-box predictors. Transferring existing explanation techniques, such as occlusion, fails as even removing a single node or edge can lead to drastic changes in the graph. The resulting graphs can differ from all training examples, causing model confusion and wrong explanations. Thus, we argue that explicability must use graphs compliant with the distribution underlying the training data. We coin this property Distribution Compliant Explanation (DCE) and present a novel Contrastive GNN Explanation (CoGE) technique following this paradigm. An experimental study supports the efficacy of CoGE.

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