Giona Casiraghi

Senior Researcher

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.

ghypernet logo

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

Giona's most recent publications

The downside of heterogeneity: How established relations counteract systemic adaptivity in tasks assignments

ArXiv (submitted for publication) - 2021

Giona Casiraghi, Christian Zingg and Frank Schweitzer

Configuration models as an urn problem

Scientific Reports 11, 13416 - 2021

Giona Casiraghi and Vahan Nanumyan

Why Online does not Equal Offline: Comparing Online and Real-World Political Support Among Politicians.

socarxiv - 2021

Laurence Brandenberger, Giona Casiraghi, Georges Andres, Simon Schweighofer and Frank Schweitzer

The likelihood-ratio test for multi-edge network models

J. Phys. Complex. 2 035012 - 2021

Giona Casiraghi

Fragile, Yet Resilient: Adaptive Decline in a Collaboration Network of Firms

Frontiers in Applied Mathematics and Statistics - 2021

Frank Schweitzer, Giona Casiraghi, Mario V. Tomasello and David Garcia

Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders

arXiv preprint arXiv:2007.06662 - 2020

Christoph Gote, Giona Casiraghi, Frank Schweitzer and Ingo Scholtes

Intervention scenarios to enhance knowledge transfer in a network of firms

Frontiers in Physics - 2020

Frank Schweitzer, Yan Zhang and Giona Casiraghi

Improving the robustness of online social networks: A simulation approach of network interventions

Frontiers in Robotics and AI - 2020

Giona Casiraghi and Frank Schweitzer

HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

Proceedings of the 2020 SIAM International Conference on Data Mining - 2020

Timothy LaRock, Vahan Nanumyan, Ingo Scholtes, Giona Casiraghi, Tina Eliassi - Rad and Frank Schweitzer

The block-constrained configuration model

Applied Network Science - 2019

Giona Casiraghi

Probing the robustness of nested multi-layer networks


Giona Casiraghi, Antonios Garas and Frank Schweitzer

A Gaussian Process-based Self-Organizing Incremental Neural Network

2019 International Joint Conference on Neural Networks (IJCNN) - 2019

X. Wang, Giona Casiraghi, Yan Zhang and J. Imura

What is the Entropy of a Social Organization?

Entropy - 2019

Christian Zingg, Giona Casiraghi, Giacomo Vaccario and Frank Schweitzer

Quantifying Triadic Closure in Multi-Edge Social Networks

ACM - 2019

Laurence Brandenberger, Giona Casiraghi, Vahan Nanumyan and Frank Schweitzer

From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles

Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part II - 2017

Giona Casiraghi, Vahan Nanumyan, Ingo Scholtes and Frank Schweitzer

see more publications