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Détection de communautés orientée sommet pour des réseaux mobiles opportunistes sociaux

Maël Canu 1
1 LFI - Learning, Fuzzy and Intelligent systems
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Our research is in the field of complex network analysis and mining, specifically addressing the communit detection task, ie. algorithms aiming to uncover particularly dense subgraphs. We focus on the implementation of such an algorithm in a decentralised and distributed context : opportunistic MANET constituted of small wireless devices using peer-to-peer communication. To tackle the implementation constraints in such networks, we propose several methods designed according to the novel and trending vertex-centred paradigm, by combining Think-Like-a-Vertex graph processing with vertex-centred community detection methods based on leaders or seeds : they show specific properties allowing dsitributed implementations suiting the opportunistic MANET case. In this context, we first a global working principle and implement it in three different algorithms dedicated to three different configurations of community detection : the VOLCAN algorithm manages the classical disjoint community detection task in a static graph. We extend it with the LOCNeSs algorithm, that is dealing with overlapping communities which means that one vertex can belong to several communities. It adds more flexibility to the method and more significance to produced results. We also tackle the dynamic graphe case (graph evolving over time), addressed by the DynLOCNeSs algorithm.Each algorithm comes with a decentralised implementation and theoretical as well as experimental studies conducted both on real and synthetic benchmark data, allowing to evaluate the quality of the results and compare to existing state-of-the-art methods. Finally, we consider a special case of opportunistic decentralised MANET developped as a part of a research project about smart and communicating clothing. We formalise a task of path finding between smart t-shirts holders and propose a recommandation strategy using community structure, that we model and evaluate through an algorithm named SWAGG.
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Submitted on : Thursday, March 29, 2018 - 3:05:51 AM
Last modification on : Thursday, June 11, 2020 - 4:27:16 PM


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  • HAL Id : tel-01745380, version 2


Maël Canu. Détection de communautés orientée sommet pour des réseaux mobiles opportunistes sociaux. Algorithme et structure de données [cs.DS]. Université Pierre et Marie Curie - Paris VI, 2017. Français. ⟨NNT : 2017PA066378⟩. ⟨tel-01745380v2⟩



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