School

The School on Network Science will take place on Tuesday, February 7. If you are planning to assist to the School, please mark the corresponding option when you fill the registration form to NetSciX’23, so that we can have an estimation of the number of assistants to each tutorial.

Lecturers

Course: Percolation on complex networks

Percolation is one of the most studied processes in statistical physics. It describes how the connectedness at the macroscopic level changes in relation with the microscopic connectivity of the individual elements in a system. Percolation models have been used to study properties of materials, such as porosity and conductivity. In network science, percolation models turned out to be very useful in the analysis of spreading phenomena in social environments, and in robustness studies of technological and infrastructural systems. The first part of the course will review traditional models of percolation in complex networks. The second part of the course will be dedicated to more recent developments in this research area, including the formulation of the so-called optimal percolation problem and the generalization of percolation models to multi-layer and higher-order networks.

Filippo Radicchi
Indiana University
USA

Yamir Moreno
Instituto BIFI y Departamento de Física Teórica
Facultad de Ciencias, Univ Zaragoza
Zaragoza, Spain

Course: Mathematical and data-driven models of infectious disease spreading

The course will discuss the mathematical and computational approaches that are most used to model the spreading of infectious diseases in both synthetic and real populations. We will present formulations that account for the structure of the networks of interactions in single and multilayer settings, discussing several limiting cases. The models will include both short-lived diseases (like influenza) and persistent infections (such as Tuberculosis) as well as several scales that range from the individual to the population level. Finally, the last part of the course will introduce data-driven models, which would allow discussion of the impact of different mitigation strategies in real pandemic scenarios like COVID-19.