Economic Resilience Prediction through Supervised Methods on Dynamic Graphs.

• Marcus Araújo University of São Paulo
• Francisco A. Rodrigues University of São Paulo
• Elaine P. Sousa University of São Paulo

DOI: https://doi.org/10.5753/sbbd.2022.226202

Abstract

The development of economic resilience has been a critical discussion for the last five years of the World Economic Forum. Moreover, since the Great Recession (2008), several articles focused on defining, measuring, and exploring factors that could differentiate economies that overcome rapidly severe interruptions from those that couldn’t. This paper uses the United Nations’ massive historical datasets to compare supervised methods under independent and dynamic graph-based representations, weighing the pros and cons of an approach that considers neighborhood information. The results report a 19% gain (F1-Score) in crisis and stability prediction.

Keywords: Dynamic Graph Resilience, Economic Resilience, Supervised Methods on Dynamic Graphs, Dynamic Graphs Mining, Complex Networks

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