Augmenting Firm Diversification Behavior Prediction with Graph Embeddings
Abstract
Public procurement plays a crucial role in modern economies, serving not only as a mechanism to acquire public goods, services, and works, but also as a policy tool to foster innovation and development. This chapter uses graph embeddings and machine learning to predict firms’ competitiveness in securing contracts by activity sector, extending activity and technological diversification methods to public procurement markets.
Type
Publication
In Complex Networks & Their Applications XIV, Studies in Computational Intelligence 1265, Springer
Cristian Candia
Associate Professor and Head of CRiSS-LAB, School of Engineering and School of Government, Universidad del Desarrollo, Chile.
My research interests include applied AI, computational social science, network science, collective intelligence, school coexistence, decision intelligence, and business analytics.
