Current graph neural networks (GNNs) that tackle node classification on
graphs tend to only focus on nodewise scores and are solely evaluated by
nodewise metrics. This limits uncertainty estimation on graphs since nodewise
marginals do not fully characterize the joint distribution given the graph
structure. In this work, we propose novel edgewise metrics, namely the edgewise
expected calibration error (ECE) and the agree/disagree ECEs, which provide
criteria for uncertainty estimation on graphs beyond the nodewise setting. Our
experiments demonstrate that the proposed edgewise metrics can complement the
nodewise results and yield additional insights. Moreover, we show that GNN
models which consider the structured prediction problem on graphs tend to have
better uncertainty estimations, which illustrates the benefit of going beyond
the nodewise setting.

本文提出新的边度量指标，包括期望校准误差（ECE）和同意 / 不同意 ECE，可用于计算图上的不确定性评估，以提供节点评分之外的观点和洞察力。同时，本文还表明，将其结构化表示问题纳入 GNN 模型中能够更好地估计不确定性，这表明超越节点级别的设定具有重要的益处。