Nicola Perra
Networks, virtually in any domain, are dynamical entities. Think for example about social networks. New nodes join the system, others leave it, and links describing their interactions are constantly changing. However, due to absence of time-resolved data and mathematical challenges, the large majority of research in the field neglects these features in favor of static representations. While such approximation is useful and appropriate in some systems and processes, it fails in many others. Indeed, in the case of sexual transmitted diseases, ideas, and meme spreading, the co-occurrence, duration and order of contacts are crucial ingredients.
During this talk, I will present a novel mathematical framework for the modeling of highly time-varying networks and processes evolving on their fabric. In particular, I will focus on epidemic spreading, random walks, and social contagion processes on temporal networks.
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Nicola Perra
Networks, virtually in any domain, are dynamical entities. Think for example about social networks. New nodes join the system, others leave it, and links describing their interactions are constantly changing. However, due to absence of time-resolved data and mathematical challenges, the large majority of research in the field neglects these features in favor of static representations. While such approximation is useful and appropriate in some systems and processes, it fails in many others. Indeed, in the case of sexual transmitted diseases, ideas, and meme spreading, the co-occurrence, duration and order of contacts are crucial ingredients.
During this talk, I will present a novel mathematical framework for the modeling of highly time-varying networks and processes evolving on their fabric. In particular, I will focus on epidemic spreading, random walks, and social contagion processes on temporal networks.
