arXiv:2605.30786v1 Announce Type: new Abstract: Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performance improvements for graph classification. However, existing GNNs are typically forced to make predictions even under high uncertainty or unknown conditions, resulting in unreliable decisions that can severely impact downstream tasks, particularly in safety-critical scenarios. To address this critical limitation, we propose AbstainGNN, a novel and theory-driven framework
The increasing deployment of GNNs in real-world, safety-critical applications necessitates solutions for their inherent uncertainty and potential for unreliable decisions.
This development addresses a critical limitation of GNNs by enabling them to abstain from making predictions when uncertain, thereby increasing their trustworthiness and applicability in high-stakes environments.
GNNs can now be designed with an explicit mechanism for 'knowing when they don't know,' fostering more robust and reliable AI systems, especially for decision-making.
- · AI developers
- · Safety-critical industries (e.g., healthcare, autonomous systems)
- · Graph data mining
- · Systems relying on unchecked GNN predictions
- · Solutions that only focus on GNN predictive accuracy without reliability
Increased adoption of GNNs in sensitive areas due to enhanced reliability.
Development of new benchmarking standards for GNNs that include abstention capabilities and uncertainty quantification.
The concept of 'abstention' extends to other AI models beyond GNNs, leading to a broader shift towards reliable and accountable AI.
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Read at arXiv cs.LG