Supervision : Bénédicte LE GRAND Co-supervision : GUILLAUME Jean-Loup
Dynamics of complex networks: analysis using intrinsic time
We are surrounded by a multitude of interaction networks from different contexts. These networks can be modeled as graphs, called complex networks. They have a community structure, i.e. groups of nodes closely related to each other and less connected with the rest of the graph. An other phenomenon studied in complex networks in many contexts is diffusion. The spread of a disease is an example of diffusion. These phenomena are dynamic and depend on an important parameter, which is often little studied: the time scale in which they are observed. According to the chosen scale, the graph dynamics can vary significantly.
In this thesis, we propose to study dynamic processes using a suitable time scale. We consider a notion of relative time which we call intrinsic time, opposed to "traditional" time, which we call extrinsic time. We first study diffusion phenomena using intrinsic time, and we compare our results with an extrinsic time scale. This allows us to highlight the fact that the same phenomenon observed at two different time scales can have a very different behavior. We then analyze the relevance of the use of intrinsic time scale for detecting dynamic communities. Comparing communities obtained according extrinsic and intrinsic scales shows that the intrinsic time scale allows a more significant detection than extrinsic time scale.
Defence : 10/10/2014 - 10h - Site Jussieu 55-65/211 Jury members : Florence SEDES, Professeur, Université de Toulouse, Rapporteur
Eric GAUSSIER, Professeur, Université Grenoble I, Rapporteur
Martine COLLARD, Professeur, Université des Antilles et de la Guyanne
Anthony PEREZ, Maître de conférences, Université d'Orléans
Marcelo DIAS DE AMORIM, Directeur de recherche, UPMC
Bénédicte LE GRAND, Professeur, Université Paris
Jean-Loup GUILLAUME, Professeur, Université de la Rochelle