A carregar...
Publicação
Predicting the Portuguese public procurement markets with graph neural networks
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 8.19 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Public procurement represents a substantial share of public expenditure and plays a central role in markets, public services, and economic policy. Yet, repeated interactions among firms and public entities raise concerns about concentration, competition, and market organization. This thesis studies Portuguese public procurement as an evolving network of firm–firm relationships across two regimes: open tenders and direct awards. Using Portal BASE data (2009-2024), two statistically validated networks are constructed. The open tenders network captures direct competition through co-bidding, while the direct awards network reflects weaker competitive proximity through shared activity in product and public-entity segments. The analysis first compares their structural properties, such as connectivity, clustering, community structure, and overlap, and then frames market evolution as a link prediction problem using heuristics, embeddings, and graph neural networks. Results show clear structural differences: open tenders are more clustered and modular, while direct awards are larger and more diffuse. Link prediction results demonstrate that future relationships can be predicted with significant precision, with BUDDY achieving the best overall performance. An exploratory perturbation analysis based on BUDDY scores further shows that procurement networks are structurally flexible under targeted link changes. Overall, this thesis shows that Portuguese public procurement can be effectively analyzed as an evolving system. By combining network analysis, link prediction, and network perturbations, it provides a framework to describe, predict, and stress-test procurement market structure, contributing to the development of forward-looking tools for monitoring, competition analysis, and risk screening.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Public procurement Open tenders Direct awards Network analysis Link prediction Graph neural networks