Complex networks and public funding: the case of the 2007-2013 Italian program

In this paper we apply techniques of complex network analysis to data sources representing public funding programs and discuss the importance of the considered indicators for program evaluation. Starting from the Open Data repository of the 2007-2013 Italian Program Programma Operativo Nazionale 'Ricerca e Competitivit\`a' (PON R&C), we build a set of data models and perform network analysis over them. We discuss the obtained experimental results outlining interesting new perspectives that emerge from the application of the proposed methods to the socio-economical evaluation of funded programs.Comment: 22 pages, 9 figure

Reconstruction, modelling and analysis of economic networks

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

Multiscale network renormalization: scale-invariance without geometry

Systems with lattice geometry can be renormalized exploiting their coordinates in metric space, which naturally define the coarse-grained nodes. By contrast, complex networks defy the usual techniques, due to their small-world character and lack of explicit geometric embedding. Current network renormalization approaches require strong assumptions (e.g. community structure, hyperbolicity, scale-free topology), thus remaining incompatible with generic graphs and ordinary lattices. Here we introduce a graph renormalization scheme valid for any hierarchy of coarse-grainings, thereby allowing for the definition of `block-nodes' across multiple scales. This approach reveals a necessary and specific dependence of network topology on additive hidden variables attached to nodes, plus optional dyadic factors. Renormalizable networks turn out to be consistent with a unique specification of the fitness model, while they are incompatible with preferential attachment, the configuration model or the stochastic blockmodel. These results highlight a deep conceptual distinction between scale-free and scale-invariant networks, and provide a geometry-free route to renormalization. If the hidden variables are annealed, they lead to realistic scale-free networks with density-dependent cut-off, assortatitivy and finite local clustering, even in the sparse regime and in absence of geometry. If they are quenched, they can guide the renormalization of real-world networks with node attributes and distance-dependence or communities. As an application, we derive an accurate multiscale model of the International Trade Network applicable across hierarchically nested geographic partitions

An hippocampal segmentation tool within an open cloud infrastructure

This study presents a fully automated algorithm for the segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI) and its deployment as a service on an open cloud infrastructure. Optimal atlases strategies for multi-atlas learning are combined with a voxel-wise classification approach. The method efficiency is optimized as training atlases are previously registered to a data driven template, accordingly for each test MRI scan only a registration is needed. The selected optimal atlases are used to train dedicated random forest classifiers whose labels are fused by majority voting. The method performances were tested on a set of 100 MRI scans provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Leave-oneout results (Dice = 0.910 ± 0.004) show the presented method compares well with other state-of-the-art techniques and a benchmark segmentation tool as FreeSurfer. The proposed strategy significantly improves a standard multi-atlas approach (p < .001)

Computer Aided Detection System for Prediction of the Malaise during Hemodialysis

Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present a Computer Aided Detection (CAD) system for the telemonitoring of patients' clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of two Random Forest (RF) classifiers: the first one (RF 1 ) predicting the onset of any malaise one hour after the treatment start and the second one (RF 2 ) again two hours later. The developed system shows an accurate classification performance in terms of both sensitivity and specificity. The specificity in the identification of nonsymptomatic sessions and the sensitivity in the identification of symptomatic sessions for RF 2 are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients

Computer Aided Detection System for Prediction of the Malaise during Hemodialysis

Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present a Computer Aided Detection (CAD) system for the telemonitoring of patients’ clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of two Random Forest (RF) classifiers: the first one (RF1) predicting the onset of any malaise one hour after the treatment start and the second one (RF2) again two hours later. The developed system shows an accurate classification performance in terms of both sensitivity and specificity. The specificity in the identification of nonsymptomatic sessions and the sensitivity in the identification of symptomatic sessions for RF2 are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients