AbstainGNN：教会图神经网络在图分类中弃权
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
AI总结：
提出AbstainGNN框架，通过理论驱动的弃权机制让GNN在不确定时拒绝预测，避免错误决策，并基于PAC-Bayes理论优化分类与弃权权衡。
摘要：
图分类是图数据挖掘中的核心任务，具有广泛的现实应用。图神经网络的最新进展显著提升了图分类的性能。然而，现有的GNN即使在高度不确定性或未知条件下也通常被迫做出预测，导致不可靠的决策，特别是在安全关键场景中会严重影响下游任务。为了解决这一关键限制，我们提出了AbstainGNN，一种新颖且理论驱动的带弃权图分类框架，使GNN能够拒绝不确定的预测，而不是产生错误的决策。具体来说，AbstainGNN显式地建模了预测函数和弃权函数，从而有效利用图结构信息。此外，与现有的启发式弃权方法不同，我们从PAC-Bayesian泛化角度理论刻画了分类错误与拒绝成本之间的权衡，并推导出用于模型优化的统一学习目标。在此理论洞察的指导下，我们进一步开发了一种高效的两阶段训练策略，包括预测函数预热和弃权函数校准。在五个基准数据集上的大量实验表明，AbstainGNN优于现有的弃权方法，在相同拒绝率下实现了更优的分类性能。
英文摘要：
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 for graph classification with abstention, which enables GNNs to reject uncertain predictions instead of producing incorrect decisions. Specifically, AbstainGNN explicitly models both the predictive function and the abstention function, allowing for effective utilization of graph structural information. Moreover, unlike existing heuristic abstention methods, we theoretically characterize the trade-off between classification errors and rejection costs from a PAC-Bayesian generalization perspective, and derive a unified learning objective for model optimization. Guided by this theoretical insight, we further develop an efficient two-stage training strategy consisting of predictive function warm-start and abstention function calibration. Extensive experiments on five benchmark datasets show that AbstainGNN outperforms existing abstention methods, achieving superior classification performance under the same rejection rates.
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