Innovating Works

TIMING

Financiado
Temporal Innovative Model for Imitative Network Generation: a framework to analy...
Temporal Innovative Model for Imitative Network Generation: a framework to analyse temporal networks and generate surrogates The TIMING project will develop a framework to analyse temporal networks and generate realistic surrogates. Observation of real-world temporal networks given as input will aim at grasping their intricate structure, resulting from... The TIMING project will develop a framework to analyse temporal networks and generate realistic surrogates. Observation of real-world temporal networks given as input will aim at grasping their intricate structure, resulting from temporal and topological causalities and correlations. I will devise a methodology to mimic these dynamics, using concepts of network science theory as guidelines. The generated synthetic networks will reproduce the fundamental features of the original topologies. This methodology will allow to obtain surrogate temporal networks to replace real data when the latter are not usable or sharable. This is particularly useful for social data, often subject to privacy issues. Moreover, since collected datasets are generally too small to observe interesting effects when simulating dynamical processes (a crucial problem, e.g., in computational epidemiology), it will serve as a tool for data augmentation. TIMING will in fact allow generating temporal networks with a higher number of nodes and a longer time span, based on original patterns extracted from smaller available datasets. TIMING will also provide the possibility to merge different original data, e.g., social interactions collected in different environments, providing a more realistic synthetic portrait of a society. Existing methods to generate realistic temporal graphs are scarce and are mainly based on blind reproduction of an original network, or they focus on specific theoretical aspects, while methods permitting an overall vision are missing. TIMING will fill this gap by combining an emulative algorithm with theoretical inductions, taking into account the mesoscale interplay between time and topology. I will develop the project in the team of Prof. Alain Barrat at CNRS (France), working at the intersection of multiple fields: physics of complex systems, network science, and statistical physics, establishing contacts with social scientists, epidemiologists and neuroscientists. ver más
31/01/2025
CNRS
131K€
Perfil tecnológico estimado
Duración del proyecto: 20 meses Fecha Inicio: 2023-05-05
Fecha Fin: 2025-01-31

Línea de financiación: concedida

El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-05-05
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022
Cerrada hace 2 años
Presupuesto El presupuesto total del proyecto asciende a 131K€
Líder del proyecto
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5