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DANTEDynamic Networks:Temporal and Structural Capture ApproachTeam leader: Paulo GonçalvesKeywords: 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)