SAIFR:2143

Machine Learning of Epidemic Processes in Networks

(2020). Machine Learning of Epidemic Processes in Networks. ICTP South American Institute for Fundamental Research. https://scivideos.org/index.php/ictp-saifr/2143

Machine Learning of Epidemic Processes in Networks. ICTP South American Institute for Fundamental Research, Mar. 03, 2020, https://scivideos.org/index.php/ictp-saifr/2143 

          @misc{ scivideos_SAIFR:2143,
            doi = {},
            url = {https://scivideos.org/index.php/ictp-saifr/2143},
            author = {},
            keywords = {ICTP-SAIFR, IFT, UNESP},
            language = {en},
            title = {Machine Learning of Epidemic Processes in Networks},
            publisher = { ICTP South American Institute for Fundamental Research},
            year = {2020},
            month = {mar},
            note = {SAIFR:2143 see, \url{https://scivideos.org/index.php/ictp-saifr/2143}}
          }
Francisco Rodrigues
Talk numberSAIFR:2143
Source RepositoryICTP – SAIFR
Collection
Talk Type Conference
Subject

Abstract

In this talk, we propose an approach based on machine learning algorithms to predict epidemic processes in complex networks. Specifically, we show that it is possible to estimate the outbreak size starting from a single node in a network and determine which networks properties are the most related to the spreading dynamics. Our approach is general and can be applied to any dynamical process running on top of complex networks. Likewise, our work constitutes an important step towards the application of machine learning methods to unravel dynamical patterns emerging in complex networked systems.