Prediction in complex systems: the case of the international trade network

TL;DR

This paper applies and enhances link prediction algorithms to forecast future international trade relationships by analyzing a complex network of countries and exported products, improving accuracy with novel similarity metrics.

Contribution

It introduces a new product similarity metric leveraging causality in network evolution, enhancing link prediction accuracy in international trade networks.

Findings

Heat and mass diffusion-based predictions are effective.

Incorporating country fitness improves prediction accuracy.

A causality-based product similarity metric yields the best results.

Abstract

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.