Time-varying (temporal) networks provide an efficient formalism to study many complex real-world systems. They notably provide a natural modeling framework of structural and functional brain networks, protein interaction networks, social interactions, information and epidemic spreading, and infrastructural and financial networks. Real-world temporal networks and dynamic processes that take place in them show heterogeneous, non-Markovian, and intrinsically correlated dynamics, making their analysis particularly challenging.
This project aims at developing versatile, robust, and scalable numerical methods for the analysis of empirical temporal networks and dynamical processes that take place in them. We currently focus on defining a general and consistent framework for numerically generated randomized reference (null) models (RRMs) for temporal networks and an automated procedure for generating and interpreting RRMs for empirical networked systems (paper here).
Past work has focused on the development of fast simulation algorithms for stochastic processes on empirical temporal networks (paper and python library incorporating the algorithms) and on methods for correcting biases due to incomplete sampling (paper).