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DANTE

Dynamic Networks:

Temporal and Structural Capture Approach

Team leader: Paulo Gonçalves

Keywords: Dynamics networkt, Graph signal processing, Computational Human Dynamics, Distributed algorithms, Wireless networks

 

Goals: Formal tools to study structures, dynamics, and performance of networks

Applications: Computational social science, Communication networks, Neuroscience, Network science

  • GRAPH SIGNAL PROCESSING AND MACHINE LEARNING

Algebraic transformations of graph signals

Machine Learning

  • COMPUTATIONAL HUMAN DYNAMICS: OBSERVATIONS, METHODS AND THEORY

Temporal networks and co-evolving processes

Effects of temporal and structural correlations

Data driven observation and models of collective phenomena

Network representations

  • DISTRIBUTED ALGORITHMS: PERFORMANCE AND ADAPTATION

System modeling to improve communications

Adaptative solutions

Main collaborations:

  • Aalto University (Finland)
  • DM2L (Liris, U. Lyon 1)
  • ISI Torino (Italy)
  • Lab. Hubert Curien (U. J. Monnet)
  • NEO (Inria Sophia-Méditerranée)
  • NPA (UPMC)
  • SiSyPHe (Lab. Phys., ENS de Lyon)