I'm a PhD researcher focused on the analysis and modelling of real-world multi-dimensional networks

A mathematical framework can be used to describe many real systems. This assumption underpins my research, which can be placed in the network science field and is focused on complex systems. These systems are made up of a large number of elementary units which, as a whole, show properties that cannot be predicted by the mere knowledge of the individual parts. The standard approach to network description of complex systems consists of studying the mathematical object, also called graph, resulting from the aggregation of all the interactions (edges) observed between a certain set of elementary units (nodes). A social system, for instance, can be described as a set of individuals through an aggregation of friendships, communications, collaborations, and working relationships, just to mention some of them. However, considering all the interactions on an equal footing might in general discard important information about the structure and functioning of the original real system. Therefore, a better description of the system is given by the so-called ``multi-layer networks'', i.e., networks where each node appears in a set of different layers, and each layer describes all the edges of a given type (e.g. a different kind of social interaction). My PhD project aims to develop new measures, based on optimisation principles and information theory techniques, to extract relevant features from large-scale multi-layer networks, by filtering out noise and redundant information. In particular, the identification of key components, which should retain the maximum amount of information, will help to speed up the process of comparison among different multilayer networks and lay the foundation for the construction of a multilayer ''backbone''. These methods can be applied to several aspects of transportation networks, biological systems, and human behaviours, including mobility and social interactions. As an example, we are currently working on the human brain network reconstructed from the medical scans (fMRI and DTI) of several patients. The aim of this research is to extract features from the mathematical formulation, which can be used to distinguish between healthy and non-healthy patients (in our case, patients with Autism Spectrum Disorder).

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