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

TL;DR

This paper introduces GGAE, a graph auto-encoder model inspired by gravity models, which predicts bilateral trade flows more accurately by leveraging graph neural networks to capture complex interactions.

Contribution

The paper presents GGAE, a novel GNN-based auto-encoder that directly predicts trade amounts, improving upon traditional gravity models by modeling complex trade relationships.

Findings

GGAE outperforms traditional gravity models in trade prediction accuracy.

Incorporating GNNs captures complex trade relationships beyond simple gravity assumptions.

The model demonstrates improved performance in empirical trade flow datasets.

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 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.