提出新方法提升图神经网络预测可靠性，同时缩小预测结果范围。
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
- 用张量分解与拓扑知识学习捕捉图数据不确定性
- 在10个真实数据集上显著降低预测集合大小，提升精度
- 适合关注模型可信度与决策解释性的图分析研究者
图神经网络（GNN）已成为分析图结构数据的主流方法，通过消息传递机制融合结构与节点特征信息。然而，近期研究指出GNN的不确定性估计存在统计不可靠问题。本文针对非交换性图数据提出一种新型不确定性量化方法，同时缩小图分类任务中的标签预测集合规模。我们提出一致化张量化拓扑神经网络（CF-T2NN），通过张量分解与拓扑知识学习，对图决策过程中的内在不确定性进行建模与解读。该方法提升了神经网络预测结果的可靠性与可解释性。在10个真实世界数据集上的实证验证表明，CF-T2NN在多个图基准测试中优于多种先进方法。本工作不仅增强了GNN框架的鲁棒不确定性量化能力，也为图结构数据分析设定了新的可靠性与精度标准。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have become the de facto standard for analyzing graph-structured data, leveraging message-passing techniques to capture both structural and node feature information. However, recent studies have raised concerns about the statistical reliability of uncertainty estimates produced by GNNs. This paper addresses this crucial challenge by introducing a novel technique for quantifying uncertainty in non-exchangeable graph-structured data, while simultaneously reducing the size of label prediction sets in graph classification tasks. We propose Conformalized Tensor-based Topological Neural Networks (CF-T2NN), a new approach for rigorous prediction inference over graphs. CF-T2NN employs tensor decomposition and topological knowledge learning to navigate and interpret the inherent uncertainty in decision-making processes. This method enables a more nuanced understanding and handling of prediction uncertainties, enhancing the reliability and interpretability of neural network outcomes. Our empirical validation, conducted across 10 real-world datasets, demonstrates the superiority of CF-T2NN over a wide array of state-of-the-art methods on various graph benchmarks. This work not only enhances the GNN framework with robust uncertainty quantification capabilities but also sets a new standard for reliability and precision in graph-structured data analysis.
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