  In the era of misinformation and information inflation, the credibility
assessment of the produced news is of the essence. However, fact-checking can
be challenging considering the limited references presented in the news. This
challenge can be transcended by utilizing the knowledge graph that is related
to the news articles. In this work, we present a methodology for creating
scientific news article representations by modeling the directed graph between
the scientific news articles and the cited scientific publications. The network
used for the experiments is comprised of the scientific news articles, their
topic, the cited research literature, and their corresponding authors. We
implement and present three different approaches: 1) a baseline Relational
Graph Convolutional Network (R-GCN), 2) a Heterogeneous Graph Neural Network
(HetGNN) and 3) a Heterogeneous Graph Transformer (HGT). We test these models
in the downstream task of link prediction on the: a) news article - paper links
and b) news article - article topic links. The results show promising
applications of graph neural network approaches in the domains of knowledge
tracing and scientific news credibility assessment.
