Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph
  Convolutional Networks for Rumor Detection
  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.
