Modeling Dynamical Processes on Complex Networks: Nicola Perra, NEU MoBs Lab (Data Management Group)

Abstract: The spreading of infectious diseases, malwares, scientific ideas, memes are just few example of real world phenomena that can be modeled as dynamical processes on networks. In some cases, like for the spreading of a pandemic, the timescale governing disease's diffusion can be decoupled from the timescale driving human contacts. In others, like for the spreading of memes on social networks, the two timescales cannot be separated. Indeed, the concurrence, duration and order of contacts are crucial ingredients for the diffusion. During my talk I will first tackle the first limit presenting a realistic predictive data-driven model that considering the time-scale separation between human interactions and force of infection simulate the global spreading of influenza like illness. I will then tackle the second limit presenting a novel mathematical framework for the modeling of highly time-varying networks and processes. In particular, I will focus on epidemic spreading, random walks, and controlling strategies on temporal networks. Bio: Nicola has a PhD in physics in the field of complex networks science and statistical mechanics. Until recently he was a post-doc at MoBs lab, he continues to stay there as an associate research scientist.