重新思考节点分类中的认知不确定性：基于信息增长视角

中文摘要 AI 辅助

认知不确定性应当随着预测器获得关于数据生成过程（DGP）的额外信息而减少。然而，现有的用于节点分类的图证据深度学习（EDL）方法通常基于图特定属性构建认知不确定性，并在下游任务（如分布外检测）上进行评估，这些任务并未测试其随DGP信息增加而可约减的性质。为使可约减性可直接检验，我们引入了一个统计框架，用于研究信息增长下的认知不确定性。该框架规定了信息增长实验协议和认知预测器的一致性准则，同时利用投影图DGP确保增长图（通常不一定提供关于同一DGP的递增信息）构成对同一潜在过程的一致观测。我们表明，EDL方法并未显式估计由单个有限图观测引起的数据不确定性，而是通过模型超参数调节认知不确定性，从而排除了一致性，这通过受控信息增长实验得到了证实。作为替代方案，我们提出了图自助集成方法，通过图重采样和随机化训练同时捕获数据不确定性和程序不确定性。在相同的实验协议下，这些集成方法展现出超越标准深度集成的认知不确定性减少。这些发现支持自助集成作为信息增长下候选的一致认知预测器。

英文摘要

Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, existing graph evidential deep learning (EDL) methods for node classification typically construct epistemic uncertainty from graph-specific properties and evaluate it on downstream tasks such as out-of-distribution detection, which do not test its reducibility as information about the DGP increases. To make reducibility directly testable, we introduce a statistical framework for studying epistemic uncertainty under information growth. Our framework specifies an information-growth experimental protocol and a consistency criterion for epistemic predictors, while using projective graph DGPs to ensure that growing graphs, which in general need not provide increasing information about the same DGP, constitute coherent observations of the same underlying process. We show that EDL methods do not explicitly estimate data uncertainty arising from a single finite graph observation and instead regulate epistemic uncertainty through model hyperparameters, precluding consistency, as corroborated by controlled information-growth experiments. As an alternative, we propose graph bootstrap ensembles, capturing both data and procedural uncertainty through graph resampling and randomized training. Under the same experimental protocol, these ensembles exhibit epistemic uncertainty reduction beyond standard deep ensembles. These findings support bootstrap ensembles as candidate consistent epistemic predictors under information growth.

发表机构

• University of Trento（特伦托大学）
• Fondazione Bruno Kessler, FBK（布鲁诺·凯斯勒基金会）
• University of Tromsø（特罗姆瑟大学）

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