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Caractériser et détecter les communautés dans les réseaux sociaux

Jean Creusefond 1
1 Equipe AMACC - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : N this thesis, I first present a new way of characterising communities from a network of timestamped messages. I show that its structure is linked with communities : communication structures are over-represented inside communities while diffusion structures appear mainly on the boundaries.Then, I propose to evaluate communities with a new quality function, compacity, that measures the propagation speed of communications in communities. I also present the Lex-Clustering, a new community detection algorithm based on the LexDFS graph traversal that features some characteristics of information diffusion.Finally, I present a methodology that I used to link quality functions and ground-truths. I introduce the concept of contexts, sets of ground-truths that are similar in some way. I implemented this methodology in a software called CoDACom (Community Detection Algorithm Comparator, that also provides many community detection tools.
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Submitted on : Tuesday, March 28, 2017 - 6:55:08 PM
Last modification on : Wednesday, November 3, 2021 - 5:08:17 AM
Long-term archiving on: : Thursday, June 29, 2017 - 6:33:21 PM


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  • HAL Id : tel-01497593, version 1


Jean Creusefond. Caractériser et détecter les communautés dans les réseaux sociaux. Réseaux sociaux et d'information [cs.SI]. Normandie Université, 2017. Français. ⟨NNT : 2017NORMC203⟩. ⟨tel-01497593⟩



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