Springer Lecture

Springer Complexity Lecture

cirospringCiro Cattuto
ISI Foundation
Italy







Ciro Cattuto is the head of the Data Science Laboratory and the Research Director of the ISI Foundation. His research focuses on measuring and modeling complex phenomena in socio-technical systems, using digital traces of human behavior from Web-based systems, on-line information networks and physical sensors. He is a founder and a principal investigator of the SocioPatterns collaboration, an international effort aimed at mapping high-resolution human contact networks with applications to social network analysis and digital epidemiology

Temporal networks of human contact: measuring, representing and modeling

The adoption of mobile technologies and wearable sensors allows to quantify human behavior at unprecedented levels of scale and detail.
Wearable sensors, in particular, are opening up a new window on human mobility at the finest resolution of individual face-to-face
interactions, impacting diverse research areas such as social network analysis and infectious disease dynamics. At the same time, the large scale and the longitudinal dimension of the empirical data bring forth new challenges for generative models and pose new approximation-generalization tradeoffs.
Here we review a 5-year long effort aimed at mapping human mobility and face-to-face proximity using wearable sensors in a variety of environments that include schools, hospitals, museums and social gatherings. We illustrate the structural complexity and temporal heterogeneities of the empirical data, and discuss the effect of these features on the dynamics of simple epidemic processes unfolding over the network. We introduce a hierarchy of summarized representations for time-varying social network data and explore the interplay between the richness in detail of the data representation and its ability to accurately model important features of spreading dynamics. Finally, in social contexts, such as schools, where both community structures and correlated activity patterns are present we show that mathematical techniques for latent factor detection can be successfully used to expose and summarize the activity-community structure of the social system.