Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation structure. However, the uncertainty caused by unreliable relations in the propagation structure is common and inevitable due to wily rumor producers and the limited collection of spread data. Most approaches neglect it and may seriously limit the learning of features. Towards this issue, this paper makes the first attempt to explore propagation uncertainty for rumor detection. Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features. The model adaptively rethinks the reliability of latent relations by adopting a Bayesian approach. Besides, we design a new edge-wise consistency training framework to optimize the model by enforcing consistency on relations. Experiments on three public benchmark datasets demonstrate that the proposed model achieves better performance than baseline methods on both rumor detection and early rumor detection tasks.

本文针对社交媒体上谣言检测的不确定性问题，首次提出了通过Edge-enhanced Bayesian Graph Convolutional Network(EBGCN)探索传播不确定性以提高谣言检测性能，并采用新的边一致性训练框架优化模型的方法。在三个公共基准数据集上进行实验证明，所提出的模型在谣言检测和早期谣言检测任务上均表现优于基准方法。

边缘增强的贝叶斯图卷积网络在谣言检测中的传播不确定性研究