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Machine learning problems in complex social networks and health data

Wednesday, November 6, 2019

2.30 p.m.

ISI seminar room 1st floor

Diego Perna, PhD-Research Fellow DIMES - University of Calabria - Italy

In this presentation, we describe the design and development of tools in the context of network science and machine learning, to analyze, characterize and ultimately describe user behaviors in OSNs. We delve into social boundary spanning theory and define a method to identify and rank users characterized by alternate behavior across OSNs. The second part of this presentation deals with machine-learning-based approaches to solve problems of learning a ranking function to identify lurkers and bots in OSNs. Whereas the last part regards the application of deep learning methods to respiratory sounds for the detection of anomalies and diseases, and to learn a low-dimensional representation for the nodes of multilayer networks.