In Chapter 1 We present the mathematical and theoretical framework to define a universally renormalizable model of complex network, which we prove to be consistent with the fitness model. We also show how the model leads to Lévy-stable fitness distributions and random scale-free networks if the hidden variables are resampled at each renormalization. By contrast, we show how the model, with fixed fitness parameters, naturally describes real-world networks. Beside the theoretical framework for the network topology, we also provide a model for the reconstruction of links weight based on a modified version of the gravity model. In Chapter 2 We apply our universally rescaling model for complex networks to two main economic networks. Firstly we analyze both the binary undirected and weighted directed World Trade Network. Secondly, we study the the elec- tronic Market for Internet Deposit for the Italian bank. The former describes trade between countries and the latter reports financial transactions between Italian banks for the period of one year. In this chapter we show how our model performs in reconstructing both topological and weighted properties of these networks and of their coarse grained representation. In Chapter 3 we apply a community detection algorithm for correlation matrices, based on Random Matrix Theory, to study community structures in the United Nations Sustainable Development Goals (UN-SDG) indicators. We discuss the issue of competing indicators which seems to be confirmed by thefounding of communities that are highly correlated internally and poorly corre- lated with the members of the external groups.

Garuccio, E. (2018). Reconstruction, modelling and analysis of economic networks.

Reconstruction, modelling and analysis of economic networks

Elena Garuccio
2018-01-01

Abstract

In Chapter 1 We present the mathematical and theoretical framework to define a universally renormalizable model of complex network, which we prove to be consistent with the fitness model. We also show how the model leads to Lévy-stable fitness distributions and random scale-free networks if the hidden variables are resampled at each renormalization. By contrast, we show how the model, with fixed fitness parameters, naturally describes real-world networks. Beside the theoretical framework for the network topology, we also provide a model for the reconstruction of links weight based on a modified version of the gravity model. In Chapter 2 We apply our universally rescaling model for complex networks to two main economic networks. Firstly we analyze both the binary undirected and weighted directed World Trade Network. Secondly, we study the the elec- tronic Market for Internet Deposit for the Italian bank. The former describes trade between countries and the latter reports financial transactions between Italian banks for the period of one year. In this chapter we show how our model performs in reconstructing both topological and weighted properties of these networks and of their coarse grained representation. In Chapter 3 we apply a community detection algorithm for correlation matrices, based on Random Matrix Theory, to study community structures in the United Nations Sustainable Development Goals (UN-SDG) indicators. We discuss the issue of competing indicators which seems to be confirmed by thefounding of communities that are highly correlated internally and poorly corre- lated with the members of the external groups.
2018
Garuccio, E. (2018). Reconstruction, modelling and analysis of economic networks.
Garuccio, Elena
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1059854
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