  The gravity models has been studied to analyze interaction between two
objects such as trade amount between a pair of countries, human migration
between a pair of countries and traffic flow between two cities. Particularly
in the international trade, predicting trade amount is instrumental to industry
and government in business decision making and determining economic policies.
Whereas the gravity models well captures such interaction between objects, the
model simplifies the interaction to extract essential relationships or needs
handcrafted features to drive the models. Recent studies indicate the
connection between graph neural networks (GNNs) and the gravity models in
international trade. However, to our best knowledge, hardly any previous
studies in the this domain directly predicts trade amount by GNNs. We propose
GGAE (Gravity-informed Graph Auto-encoder) and its surrogate model, which is
inspired by the gravity model, showing trade amount prediction by the gravity
model can be formulated as an edge weight prediction problem in GNNs and solved
by GGAE and its surrogate model. Furthermore, we conducted experiments to
indicate GGAE with GNNs can improve trade amount prediction compared to the
traditional gravity model by considering complex relationships.
