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, Data Science Institute, School of Engineering, Universidad del Desarrollo, Chile. Head of CRiSS-LAB.
Cristian Candia studies how societies transform information into collective relevance through attention, memory, preferences, and coordination. His work combines computational social science, network science, AI, and large-scale behavioral data to understand how groups, institutions, and societies decide what matters.
