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.
Delft University of Technology
Faculty of Technology, Policy and Management
Building 31
Jaffalaan 5
2628 BX Delft
The Netherlands
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the
Marie Skłodowska-Curie grant agreement number 955708.