Temporal Centralities: A study of the importance of nodes in dynamic graphs
Nowadays, interactions are a huge part of our daily life. These interactions can represent the diffusion of rumors, diseases, etc. Understanding how these interactions affect our life is quite important. A natural way to do so is using graph theory. However, this is not straightforward as studies show the temporal aspect, in other words, the order of interactions, should be taken into account. In this work, we concentrated on detecting the important individuals in these graphs using centrality metrics that take into account the temporal aspect. We proposed a comparison protocol that compares the different centrality metrics that exist. We applied it on several networks, which gave us insight on how the different metrics react. Secondly, we observed the high computational need of these centrality metrics. Therefore, we introduced a method to reduce this need. And finally, we introduced a novel centrality metric that we call ego-betweenness centrality.
Defence : 10/05/2018 - 11h - Site Jussieu 25/26-105 Jury members : Vania Conan, Responsable de Recherche HDR, THALES [Rapporteur]
Jean-Philippe Cointet, Professeur Sciences Po [Rapporteur]
Marcelo Dias de Amorim, Directeur de recherches, CNRS
Nathalie Mitton, Directeur de recherche, HDR, INRIA
Clémence Magnien, Directeur de recherches, CNRS
Fabien Tarissan, Chargé de recherche, CNRS