MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation

• URL: http://arxiv.org/abs/2405.12519v1
• Date: Tue, 21 May 2024 06:12:24 GMT
• Title: MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
• Authors: Zhaoning Yu, Hongyang Gao,
• Abstract summary: Graph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging. Traditional model-level explanation methods like XGNN and GNNInterpreter often fail to identify valid substructures like rings, leading to questionable interpretability. We introduce an innovative textbfMotif-btextbfAsed textbfGNN textbfExplainer (MAGE) that uses motifs as fundamental units for generating explanations.
• Score: 16.129359492539095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging. Traditional model-level explanation methods like XGNN and GNNInterpreter often fail to identify valid substructures like rings, leading to questionable interpretability. This limitation stems from XGNN's atom-by-atom approach and GNNInterpreter's reliance on average graph embeddings, which overlook the essential structural elements crucial for molecules. To address these gaps, we introduce an innovative \textbf{M}otif-b\textbf{A}sed \textbf{G}NN \textbf{E}xplainer (MAGE) that uses motifs as fundamental units for generating explanations. Our approach begins with extracting potential motifs through a motif decomposition technique. Then, we utilize an attention-based learning method to identify class-specific motifs. Finally, we employ a motif-based graph generator for each class to create molecular graph explanations based on these class-specific motifs. This novel method not only incorporates critical substructures into the explanations but also guarantees their validity, yielding results that are human-understandable. Our proposed method's effectiveness is demonstrated through quantitative and qualitative assessments conducted on six real-world molecular datasets.

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