This paper studies graph-structured prediction for supervised learning on graphs with node-wise or edge-wise target dependencies. To solve this problem, recent works investigated combining graph neural networks (GNNs) with conventional structured prediction algorithms like conditional random fields. However, in this work, we pursue an alternative direction building on the recent successes of diffusion probabilistic models (DPMs). That is, we propose a new framework using DPMs to make graph-structured predictions. In the fully supervised setting, our DPM captures the target dependencies by iteratively updating each target estimate based on the estimates of nearby targets. We also propose a variational expectation maximization algorithm to train our DPM in the semi-supervised setting. Extensive experiments verify that our framework consistently outperforms existing neural structured prediction models on inductive and transductive node classification. We also demonstrate the competitive performance of our framework for algorithmic reasoning tasks.

翻译：本文研究图结构预测问题，针对具有节点级或边级目标依赖关系的监督学习任务。为解决该问题，近期研究探索了将图神经网络（GNN）与条件随机场等传统结构预测算法相结合的方法。然而，本研究基于扩散概率模型（DPM）的最新进展，提出了另一种路径：即构建一个利用DPM进行图结构预测的新框架。在全监督设置下，我们的DPM通过基于邻近目标估计值迭代更新每个目标估计，从而捕获目标间的依赖关系。我们还提出了一种变分期望最大化算法，用于在半监督设置下训练我们的DPM。大量实验证明，我们的框架在归纳式和直推式节点分类任务上持续优于现有神经结构预测模型。此外，我们还展示了该框架在算法推理任务中的竞争性表现。