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Domain Machine Learning-Robotics
Domain - extra Machine Learning models of Grids
Year 2010
Starting October 2010
Status Closed
Subject Characterizing the spatiotemporal structure and dynamics of e-science social networks
Thesis advisor GERMAIN-RENAUD Cécile
Laboratory LRI A&O
Abstract A complex system consists of many interacting units, whose collective behavior cannot be explained from the behavior of the individual units alone. Complex dynamical networks are complex systems that can be represented with graphs dynamically evolving in time. Computational grids provide new natural examples of large-scale complex networks emerging from collective behavior. Moreover, computational grids feature multiple levels of interactions. An interesting question is thus whether these networks will exhibit properties similar to those of social networks, or original ones, which would be the specific signature of e-science. An operational question is the creation of generative models appropriate for forecasting future graph structure. The PhD will characterize the spatiotemporal structure of the graphs created 1) by co-access to files, and 2) by the job traffic.
Work program
Extra information Sujet (en anglais)/Subject (in English) at http://www.lri.fr/~cecile/Stages/ThesisCNCGR.pdf
Le candidat peut être francophone ou anglophone.
Prerequisite M2 d'informatique/Master in Computer Science

Details Download TheseComplexNetworks.pdf
Expected funding Institutional funding
Status of funding Expected
user cecile.germain-renaud
Created Monday 22 of February, 2010 20:53:28 CET
LastModif Friday 18 of June, 2010 14:32:55 CEST

Ecole Doctorale Informatique Paris-Sud

Nicole Bidoit
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info at lri.fr