Predicting epidemic spreading and synchronization from network structure

Thursday, October 25, 2018

12.00 p.m.

ISI seminar room 1st floor

Prof. Francisco Aparecido Rodrigues University of São Paulo

One of the most fundamental problems in Network Science is to understand how dynamical processes are influenced by the network organization. For instance, if we can understand how patterns of connections between coupled oscillators influence the evolution of the synchronous state, then we can change the network topology to control the level of synchronization of power grids and electronic circuits. Despite this fundamental importance, predicting the dynamical variable associated to a dynamical process, as the state of an oscillator or the outbreak size, from the network structure is a very complicated task in structured networks due to the presence of non-trivial patterns of connections, nonlinear effects and correlations between variables. However, we can gain some insights about the influence of network structure on dynamical processes by using mean-field approximation and machine learning algorithm. In this talk, we will show how the critical threshold for the emergence of synchronization and the critical probability for the occurrence of the endemic state depend on network properties. Moreover, we will how machine learning methods can be used to predict the outbreak size starting from a single node and the state of Kuramoto oscillators. The current challenges in Network Science and some possible ideas for future research will also be discussed in our talk.

Francisco Aparecido Rodrigues is an Associate Professor of Applied Mathematics at the Institute of Mathematics and Computer Science, University of São Paulo, and leader of the Complex Systems Group. Currently, he is a Leverhulme visiting professor (one-year sabbatical) at the University of Warwick, Mathematics Institute, working at the Centre for Complexity Science. I was awarded a Leverhulme Visiting Professorship. His research activities are in complex systems, complex networks, epidemic models, synchronization, statistical inference of networks and data mining. Particularly, he is interested in studying how the network structure influences the evolution of dynamical processes, like synchronization of coupled oscillators or the spreading of disease in social networks