We use network and data science to study structure, dynamics and resilience of real-world systems
Theoretical advance on the representation of complex networks for modeling empirical complex systems, identifying central/influential units and determine the underlying meso-scale organization.
Single and coupled dynamics on multilayer networks for modeling information/awareness propagation, complex contagion, epidemics spreading, consensus mechanisms. Our goal is to better understand robustness, resilience and emergence of collective phenomena in complex networked systems.
Information theory is intimately realted to statistical physics, playing a key role in data science and a variety of applications. We develop theoretical and analytical tools to quantify how complex networks produce and process information, to reduce their dimensionality.
Network geometry is rapidly gaining attention for providing a suitable framework for the analysis of interacting systems. We focus on the application of network diffusion maps to better understand the dynamics of spreading processes and to provide coarse-grained representation of networkd systems.
Resources, references and explanations about COVID19
Monitor the misinformation risk worldwide
The analysis of millions check-ins in megacities reveals complex spatio-temporal patterns characterizing multirelational human activities.
Our work on unraveling the functional organization of megacities from social media data has co-won the challenge.
Collective response on social media to California earthquakes in July 2019
Our work on social integration of Syrian refugees in Turkey to decrease the risk for measles outbreak was awarded the 1st prize. A great collaboration with DPCS and MOBS research units at FBK.
Blockchain and Fintech Network Science: a Satellite of NetSci 2018