Latent drivers for dynamic networks

You are cordially invited to attend the PhD Dissertation Defence of Igor Artico on Thursday 25 May 2023 at 13:30 in room D1.15 (East Campus).

Abstract:
Over the past few decades, network analysis has gained popularity in various fields, and understanding the dynamics of networks has become crucial. This thesis explores the dynamics of networks through a statistical approach, focusing on latent drivers that underlie network evolution. The thesis builds upon various key projects, each of which explores different aspects of network dynamics. The first project proposes a statistical testing procedure to determine whether the degree distribution of a given network follows a preferential attachment process, i.e., a power-law marginal distribution. The second project focuses on dynamic networks where the relational events constitute time-stamped edges and proposes a dynamic latent space relational event model, leveraging a Kalman filter EM algorithm. The third project extends it and addresses the challenge of dealing with huge relational event networks, using machine learning optimization tools. The three projects investigate the complex phenomenon of network growth and transformation, shedding light on the role of latent drivers that shape the structure of observed networks. By studying the underlying drivers, analysts can better understand how networks impact various domains.

Dissertation Committee:
- Prof. Ernst Wit, Università della Svizzera italiana, Switzerland (Research Advisor)
- Prof. Michael Multerer, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Stefan Wolf, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Alessandro Lomi, Università della Svizzera italiana, Switzerland (External Member)
- Prof. Veronica Vinciotti, University of Trento, Italy (External Member)