Recruiting beneficiary: ISI Foundation, Italy
Internal supervisors: Dr. Daniela Paolotti, Dr. Michele Tizzoni
Brief project description: In this project, we will explore a node-embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks – rather than of the network structure itself – with the goal of enabling prediction tasks related to the evolution and outcome of these processes. These embedding vectors are applicable as feature vectors in machine learning applications and yield improved performance for tasks such as node classification, link prediction, clustering, or visualization. This work will allow us to estimate temporal evolution of the entire system from sparse observations, consistently across several data sets and across a broad range of parameters of an epidemic model.