Dr. Giona Casiraghi

Research Interests

I work on two tighly connected topics. How to define and quantify resilience, in complex systems in general and in social organisations in particular. How to model complex systems from the data, to perform a data-driven study of their properties.

Resilience of Social Organisations

We aim at an understanding of how different online social organisations withstand and recover from internal and external shocks, and how such a resilience can be improved. To do, so we investigate how different resilience metrics can be computed from data. Our research is deeply rooted in data science and the data-driven modelling of complex systems. We use and develop new methodologies from network science, multivariate statistics, machine learning, and statistical physics.

Statistical Models for Network Data

To allow the study of complex dynamical systems such as social organisations, we need inferential models aimed at temporal and multi-edge network data. I am interested in the development of quantitative methods for the analysis of repeated interactions arising in social, economical, and political networks. A second line of research I follow focuses on modelling and understanding temporal correlation observed in temporal network data, such as distribution networks and communication networks.

Part of my work revolves around the generalised hypergeometric ensemble of random graphs, gHypEG for short. In its simplest form, gHypEG provides a model preserving vertices' activities. Doing so, it maps the standard configuration model to an urn problem. Pairs of nodes are like balls in an urn. The more frequent are specific balls, the more likely are edges to be sampled. In its general form, edge probabilities are again defined by balls' frequencies, but also by independent edge propensities estimated from data. The higher the propensity, the larger the ball, the easier it is to sample the edge. The main applications of gHypEG, are towards the inference of significant relations from observed interactions, and the analysis of complex networks by means of network regressions.

The R package ghypernet provides an Open Source implementation for R of a set of functions to work with gHypEG models.

GHYPERNET Tutorials and Material

The following links contain a collection of tutorials and material about the ghypernet R package.

Tutorial to network regression models: https://sg.ethz.ch/nrm-tutorial/

Companion git repository for the 2019 EUSN Workshop in Zurich: https://github.com/sg-dev/EUSN2019_r-ghypernet

Repository for the EuroCSS Tutorial “Introduction to Multi-edge Network Inference in R Using the Ghypernet-package”: https://github.com/sg-dev/EuroCSS2019_r-ghypernet

GitHub repository for ghypernet: https://github.com/gi0na/r-ghypernet

Manual and Vignettes of the R package: https://ghyper.net