The TIMING project has been devoted to developing a new framework for generating realistic temporal networks based on real-world temporal data of interactions. The generated networks serve as surrogates to replace and extend real data when the latter are not usable, sharable, or large enough. I have developed the project under the supervision of Prof. Alain Barrat, Centre de Physique Théorique (CPT) at CNRS Marseille (France).
Networks are a reference representation tool in the physics of complex systems, able to describe systems composed of multiple interacting agents. Formed by a set of discrete nodes and the connections between them, networks schematise the existing interactions among elements, providing a representative picture of the system architecture. Network science has revolutionised data analysis and modelling by introducing a new way to describe relationships between constituent elements in many disciplines, from physics to sociology, biology, and economy.
In many cases, agents’ interactions undergo a temporal evolution, with links appearing and disappearing over time, and their description requires temporal networks. This framework is fundamental in many settings, like neuronal functions and ecosystems, but is particularly useful to describe social contexts, where connections among people spontaneously change over time, both in physical and remote interactions, with non-trivial temporal correlations and structures.
Both static and temporal networks allow to schematise dynamical processes that can be simulated on their discrete topologies, including, for instance, spreading phenomena (of diseases, opinions or information), transportation models, communication, synchronisation, and consensus formation. Evidence in the literature suggests that the properties of these collective behaviours strongly depend on the structure of the underlying network and its temporal evolution. Hence, an accurate description of these processes requires temporal networks able to reflect real-world time-depending patterns. Unfortunately, information about real temporal sequences of interactions is usually incomplete due to the difficulty of collecting suitable datasets, and only very small real temporal networks are typically obtained.
In this context, synthetic networks that mimic the observed complex patterns of real structures can serve as surrogate substrates on which to simulate processes. Such surrogates can be generated with a different temporal extension from the one that it takes as input and can therefore be used for augmenting data, providing a solution to the problem of data with limited duration.
The main goal of TIMING has been to develop an algorithm to generate such synthetic networks.
