BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks

BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks

Abstract Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as “black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance. Many interpretable GNN methods exist, but they cannot quantify uncertainty i...

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Bibliographic Details
Main Authors: Whitney Sloneker, Shalin Patel, Hung-Jen Wang, Lorin Crawford, Ritambhara Singh
Format: Article
Language:English
Published: Springer 2025-06-01
Series:Journal of Statistical Theory and Applications (JSTA)
Subjects:
Deep learning
Graph neural networks
Probabilistic models
Explainability
Variational inference
Online Access:https://doi.org/10.1007/s44199-025-00118-x
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https://doi.org/10.1007/s44199-025-00118-x

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