为图神经网络的不确定性量化提供理论指导与高效实现方案。
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
- 提出适用于大规模图数据的可扩展性改进方法。
- 系统分析现有方法的设计选择并验证其优劣。
- 适合关注图模型可靠性与可信推理的研究者。
置信预测在机器学习不确定性量化中日益受到关注。近期关于图结构不确定性量化的工作基于该方法开展了图置信预测研究。然而，这一领域尚处于早期阶段，导致实现方式、基线设置和评估方法存在分歧。本文分析了文献中的设计选择，讨论了现有方法的权衡。在此基础上，我们提出技术改进，使现有方法能在不损失性能的前提下扩展至大规模图数据集。理论与实证结果验证了对未来图置信预测研究的建议。
原文摘要 · Abstract (English)
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal graph prediction. The nascent nature of these explorations has led to conflicting choices for implementations, baselines, and method evaluation. In this work, we analyze the design choices made in the literature and discuss the tradeoffs associated with existing methods. Building on the existing implementations, we introduce techniques to scale existing methods to large-scale graph datasets without sacrificing performance. Our theoretical and empirical results justify our recommendations for future scholarship in graph conformal prediction.
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