  Social media has been rapidly developing in the public sphere due to its ease
of spreading new information, which leads to the circulation of rumors.
However, detecting rumors from such a massive amount of information is becoming
an increasingly arduous challenge. Previous work generally obtained valuable
features from propagation information. It should be noted that most methods
only target the propagation structure while ignoring the rumor transmission
pattern. This limited focus severely restricts the collection of spread data.
To solve this problem, the authors of the present study are motivated to
explore the regionalized propagation patterns of rumors. Specifically, a novel
region-enhanced deep graph convolutional network (RDGCN) that enhances the
propagation features of rumors by learning regionalized propagation patterns
and trains to learn the propagation patterns by unsupervised learning is
proposed. In addition, a source-enhanced residual graph convolution layer
(SRGCL) is designed to improve the graph neural network (GNN) oversmoothness
and increase the depth limit of the rumor detection methods-based GNN.
Experiments on Twitter15 and Twitter16 show that the proposed model performs
better than the baseline approach on rumor detection and early rumor detection.
