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arXiv AI By Mahtab Sarvmaili

MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation

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The paper introduces MPGE, a Multi‑Perspective Graph Explainer that provides three complementary explanations—factual support, counterfactual sensitivity, and exemplar tolerance—for graph neural network predictions on molecular data. It formulates a shared constrained optimization that balances prediction preservation, compactness, and edit cost, and extends CF‑GNNExplainer to learn symmetric edge rankings for counterfactuals. Experiments on several benchmark datasets show that factual masks retain a small fraction of edges while counterfactual and exemplar explanations achieve high coverage, demonstrating the method’s ability to reveal model behavior without claiming chemical validity.

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