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图监督学习。为解决该问题，近期工作探索了将图神经网络（GNNs）与条件随机场等传统结构预测算法相结合。然而，本文沿袭扩散概率模型（DPMs）的最新成功，另辟蹊径提出基于DPMs的图结构预测新框架。在完全监督设定下，我们的DPM通过迭代更新每个目标估计值（基于邻近目标的估计结果）来捕捉目标依赖关系。在半监督设定下，我们提出变分期望最大化算法训练该DPM。大量实验证明，在归纳式与直推式节点分类任务中，我们的框架始终优于现有神经结构预测模型。此外，在算法推理任务中，该框架亦展现出具有竞争力的性能表现。