Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder

Abstract

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 Autoencoder) 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.

Harnessing Graph Neural Networks to Predict International Trade Flows

“…These models have exhibited their effectiveness across a range of applications, including document labeling, traffic forecasting, and fraud detection [23]. Recently, some research has also used GNN to address the challenges related to international trade in products and services [24][25][26].…”

“…New methods utilizing causality have been recently introduced to overcome these limitations and enhance the model's generalization capabilities. For example, Minakawa et al [25] proposed a GGAE (Gravity-informed Graph Auto-encoder) and its surrogate model, which draws inspiration from the gravity model. They demonstrated that the surrogate model can solve the edge weight prediction problem in GNNs and that the gravity model's prediction of trade value can be expressed as an edge weight prediction problem [30].…”