| KEYWORD |
Data-driven Identification of Influence in Social Networks
keywords COMPRESSED SENSING, IDENTIFICATION, SYSTEMS THEORY
Reference persons GIUSEPPE CARLO CALAFIORE, FABRIZIO DABBENE
Description Goal of the thesis is to estimate the network structure and the strength of the relations and trust among agents starting from the observation of preferences. In particular, the thesis will focus on the analysis of the roll-call data in the Italian Senate. A recent thesis mined the key voting records of the Italian Senate during the XVII legislature in order to extract the hidden information about the closeness of senators to political parties, based on a parsimonious feature extraction method that selects the most relevant bills, and derived an information theoretic measure, which we refer to as Political Data-aNalytic Affinity (Political DNA). The concept of Political Map was also introduced.
In this thesis, the student will apply graph-based techniques to model and possibly predict the affinity between Senators. The goal is to obtain a representation of the network of influences of the Senators.
See also cnr - thesis proposal on social dynamical networks.pdf
Required skills Basic mathematical modeling, Basic signal processing, Convex optimization, Basic coding skills (MATLAB is
enough).
Notes The thesis will be carried out under the supervision of the staff of the System and Modeling Control Group at the Institute of Electronics, Computer and Telecommunication Engineering of National Research Council of Italy (IEIIT-CNR), in collaboration with Prof. Giuseppe Calafiore, Dept. of Electronic Engineering, Politecnico di Torino, and the association OpenPolis (www.openpolis.it).
Deadline 20/11/2019
PROPONI LA TUA CANDIDATURA