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

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.

References in corpus (5)

• The Building Blocks of Economic Complexity
• The Product Space Conditions the Development of Nations
• Heat Conduction Process on Community Networks as a Recommendation Model
• The International Trade Network: weighted network analysis and modelling
• Adaptive model for recommendation of news

Cited by in corpus (5)

• Nestedness in complex networks: Observation, emergence, and implications
• Network-based recommendation algorithms: A review
• Measuring economic complexity of countries and products: which metric to use?
• The mathematics of non-linear metrics for nested networks
• Enhancing countries' fitness with recommender systems on the international trade network