New GNN framework teaches models to abstain from uncertain predictions

Researchers have developed AbstainGNN, a new framework designed to improve the reliability of graph neural networks (GNNs) in graph classification tasks. This novel approach allows GNNs to abstain from making predictions when faced with high uncertainty, a critical feature for safety-critical applications. AbstainGNN explicitly models both prediction and abstention functions, theoretically characterizing the trade-off between errors and rejection costs from a PAC-Bayesian perspective. An efficient two-stage training strategy is employed, and experiments show it outperforms existing abstention methods. AI

IMPACT Enhances reliability of GNNs in critical applications by enabling abstention from uncertain predictions.

RANK_REASON The cluster contains an academic paper detailing a new method for graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• AbstainGNN
• Graph Neural Networks
• PAC-Bayesian

• paper
• safety

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