论文标题
图不仅仅是其节点：朝着图上的结构不确定性学习学习
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
论文作者
论文摘要
当前的图形神经网络（GNNS）在图上处理节点分类的当前图倾向于侧重于nodeWise评分，并且仅通过nodeWise指标评估。这限制了图表上的不确定性估计，因为截至图形结构，割线的边缘没有完全表征关节分布。在这项工作中，我们提出了新颖的边缘指标，即EdgeWise预期的校准误差（ECE）和同意/分歧ECE，这为不确定性估计的标准估算了脱节设置以外的图形。我们的实验表明，所提出的边缘度量指标可以补充割点结果并产生其他见解。此外，我们表明，考虑图上的结构化预测问题的GNN模型往往具有更好的不确定性估计，这说明了超越点式设置的好处。
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.
