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  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Full Professor
  • Graduate Student
  • Lab assistant
  • Non-permanent Researcher
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
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Starting Date
03
Oct 2016
Ending Date
31
Dec 2021
Status
Ongoing
Members
1
Structures
2
Publications
2

About

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 networks and dynamic processes that take place in them show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics, making their analysis particularly challenging.

This project aims at developing versatile, robust, and scalable numerical methods for the analysis of empirical networks and dynamical processes that take place in them. We have recently developed a general and consistent framework for numerically generated randomized reference (null) models (RRMs) for  networks, in particular networks with a time-evolving topology, providing procedures for generating, applying and interpreting RRMs for empirical networked systems (preprint here). Current work focuses on applying this framework to uncover and characterize computational motifs in the microcircuitry of the Drosophila larva brain.

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).

Fundings