News

Every economic transaction can be seen as the interaction between multiple parties, which results in a network when aggregated to the market level. Viewing economic transactions as networks enables us to utilize recent developments in graph neural networks to better understand the underlying economic process. Graph neural networks make use of local information and the structural role that a node plays in the network, which is crucial in understanding transactions, but is not adequately captured by conventional methods used in economics. The high flexibility of this approach enables the modelling of a multitude of economic questions.

Given the distinctive properties inherent to economic data, such as (apparent) infinite mean phenomena, methods developed in a purely theoretical, computer science or statistics framework must be adapted to cope with these requirements. A deeper understanding of the economic processes will help to choose and develop appropriate algorithms for information propagation through network-like structures.