  The spread of fake news has caused great harm to society in recent years. So
the quick detection of fake news has become an important task. Some current
detection methods often model news articles and other related components as a
static heterogeneous information network (HIN) and use expensive
message-passing algorithms. However, in the real-world, quickly identifying
fake news is of great significance and the network may vary over time in terms
of dynamic nodes and edges. Therefore, in this paper, we propose a novel
Dynamic Heterogeneous Graph Neural Network (DHGNN) for fake news quick
detection. More specifically, we first implement BERT and fine-tuned BERT to
get a semantic representation of the news article contents and author profiles
and convert it into graph data. Then, we construct the heterogeneous
news-author graph to reflect contextual information and relationships.
Additionally, we adapt ideas from personalized PageRank propagation and dynamic
propagation to heterogeneous networks in order to reduce the time complexity of
back-propagating through many nodes during training. Experiments on three
real-world fake news datasets show that DHGNN can outperform other GNN-based
models in terms of both effectiveness and efficiency.
