图上不确定性量化的线性意见池化方法
Linear Opinion Pooling for Uncertainty Quantification on Graphs
AI总结：
本文针对图上半监督节点分类的不确定性量化问题，提出用狄利克雷分布混合表示认知不确定性，并利用线性意见池化在相邻节点间传播信息，实验验证了方法的有效性。
摘要：
我们研究图结构数据的不确定性量化问题，更具体地说，是量化（半监督）节点分类中的预测不确定性问题。这方面的关键问题涉及区分两种不同类型的不确定性，即偶然不确定性和认知不确定性，以及如何利用图拓扑提供的结构信息来支持不确定性量化。针对现有方法中具有挑战性的假设和公设，我们提出了一种新方法，以狄利克雷分布的混合形式表示（认知）不确定性，并借助既定的线性意见池化原则在图中的相邻节点之间传播信息。该方法在一系列图结构数据集上的实验中证明了其有效性。
英文摘要：
We address the problem of uncertainty quantification for graph-structured data, or, more specifically, the problem to quantify the predictive uncertainty in (semi-supervised) node classification. Key questions in this regard concern the distinction between two different types of uncertainty, aleatoric and epistemic, and how to support uncertainty quantification by leveraging the structural information provided by the graph topology. Challenging assumptions and postulates of state-of-the-art methods, we propose a novel approach that represents (epistemic) uncertainty in terms of mixtures of Dirichlet distributions and refers to the established principle of linear opinion pooling for propagating information between neighbored nodes in the graph. The effectiveness of this approach is demonstrated in a series of experiments on a variety of graph-structured datasets.
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