The Project


DataRedux is an ANR-funded project that focuses on developing radically new methods for the reduction of the complexity of large networked datasets to feed effective and realistic data-driven models of spreading phenomena. Many rich datasets on actions and interactions of individuals have recently become available, commonly encoded as networked systems, arising from heterogeneous sources with details at different scales and resolutions, and potentially containing geographical and temporal information as well as metadata. These outstanding sources of information and knowledge fuel a wide spectrum of data-driven numerical simulations of dynamical processes. Data alone, however, even in huge amounts, do not easily transform into knowledge or predictive models. The rich and diverse information they contain raises crucial challenges concerning their analysis, representation and interpretation, the extraction of meaningful structures, and their integration into data-driven models. In this context, DataRedux puts forward an innovative framework to reduce networked data complexity while preserving its richness, by working at intermediate scales (“mesoscales”). Our objective is to reach a fundamental breakthrough in the theoretical understanding and representation of rich and complex networked datasets for use in predictive data-driven models for decision making and actionable insights.​

Duration: 2020-2023
​CNRS
The CNRS team is part of the Centre de Physique Théorique in Marseille. It is led by Alain Barrat, also coordinator of DATAREDUX. His research focuses on complex networks and the attached dynamical processes, with interdisciplinary applications (analysis of technological networks, understanding of consensus in social networks, epidemic spreading phenomena, etc.).

ENS-Lyon
The ENS Lyon team is coordinated by Paulo Gonçalves, an Inria Research Director expert in graph signal processing and machine learning. External PI of the team is Márton Karsai from the Central European University. His research focuses on complex networks and human dynamics, in particular cooperative behaviour, temporal networks, and social contagion phenomena.

​INSERM
​The INSERM team participating to the project is part of the Pierre Louis Institute of Epidemiology and Public Health, Surveillance and Modeling of infectious diseases. The team is led by Vittoria Colizza, expert in the area of modelling infectious disease epidemics (e.g. flu pandemic, SARS, Ebola, livestock infections) through the integration of large databases characterising hosts behaviour, interaction and mobility.
​News
  • DATAREDUX KO meeting will take place on April 16-17 in Marseille

​Highlights


​Publications