Session 3 - Fall 2024 will take place on Wednesday, 4:00 PM CET / 3:00 PM GMT 13 November 2024, on Teams Meeting
About The Series
The CIVICA Data Science Seminar series is a unique multi-disciplinary series focused on applications and methodologies of data science for the social, political, and economic world.
Session 3 Fall 2023 Details: Abstract from the speaker
Surrogate networks can constitute suitable replacements for real networks, in particular to study dynamical processes on networks, when only incomplete or limited datasets are available. As empirical datasets most often present complex features and interplays between structure and temporal evolution, creating surrogate data is however a challenging task, in particular for data describing time-resolved interactions between agents. Here we propose a method to generate surrogate temporal networks that mimic such observed datasets. The method is based on a decomposition of the original dataset into small temporal subnetworks encoding local structures on a short time scale. These are used as building blocks to generate a new synthetic temporal network that will hence inherit the shape of local interactions from the dataset. Moreover, we also take into account larger scale correlations on structural and temporal dimension, using them to inform the process of assembling the building blocks. We showcase the method by generating surrogate networks for several datasets of social interactions and comparing them to the original data on two complementary aspects. First, we show that the surrogate data possess complex structural and temporal features similar to the ones of the original data. Second, we simulate several dynamical processes, describing respectively epidemic spread, opinion formation and emergence of norms in a population, and compare the outcome of these processes on the generated and original datasets. We describe the method in detail and provide an implementation so that it can be easily used in future works based on temporally evolving networks.
Seminar Speaker
Giulia Cencetti
Giulia Cencetti is a Marie Curie Postdoctoral Fellow at Centre de Physique Théorique, CNRS. She has a rich and interdisciplinary background: physicist by training, she obtained a PhD in Information Engineering at the University of Florence (Italy) working on complex systems and in particular on dynamical systems on complex networks. After PhD, she spent some months at the Department of Networks and Data Science (DNDS) at the Central European University at Budapest where she started to explore the new emerging mathematical branch of higher-order networks. In FBK she is exploiting her experience and theoretical knowledge to study social applications: her research spans from urban science to human face-to-face interactions, as far as tracing strategies in the epidemic. She received the 2021 Emerging Researcher Award from the Complex System Society.
Agenda
Welcome Introduction Lorenzo Lucchini, Bocconi
Setting the scene: Brief intro to the speaker and her talk
Seminar Session Giulia Cencetti, CNRS
Generating surrogate temporal networks from mesoscale building blocks
Research Discussion. Lead Institution
Q&A / Discussion on the research
Announcement
Upcoming seminar in the series and other announcements