  Recent efforts in fake news detection have witnessed a surge of interest in
using graph neural networks (GNNs) to exploit rich social context. Existing
studies generally leverage fixed graph structures, assuming that the graphs
accurately represent the related social engagements. However, edge noise
remains a critical challenge in real-world graphs, as training on suboptimal
structures can severely limit the expressiveness of GNNs. Despite initial
efforts in graph structure learning (GSL), prior works often leverage node
features to update edge weights, resulting in heavy computational costs that
hinder the methods' applicability to large-scale social graphs. In this work,
we approach the fake news detection problem with a novel aspect of social graph
refinement. We find that the degrees of news article nodes exhibit distinctive
patterns, which are indicative of news veracity. Guided by this, we propose
DECOR, a novel application of Degree-Corrected Stochastic Blockmodels to the
fake news detection problem. Specifically, we encapsulate our empirical
observations into a lightweight social graph refinement component that
iteratively updates the edge weights via a learnable degree correction mask,
which allows for joint optimization with a GNN-based detector. Extensive
experiments on two real-world benchmarks validate the effectiveness and
efficiency of DECOR.
