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Dessin de graphe distribué par modèle de force : application au Big Data

Abstract : Graphs, usually used to model relations between entities, are continually growing mainly because of the internet (social networks for example). Graph visualization (also called drawing) is a fast way of collecting data about a graph. Internet graphs are often stored in a distributed manner, split between several machines interconnected. This thesis aims to develop drawing algorithms to draw very large graphs using the MapReduce paradigm, used for cluster computing. Among graph drawing algorithms, those which rely on a physical model to compute the node placement are generally considered to draw graphs well regardless of the type of graph. We developped two force-directed graph drawing algorithms in the MapReduce paradigm. GDAD, the fist distributed force-directed graph drawing algorithm ever, uses pivots to simplify computations of node interactions. MuGDAD, following GDAD, uses a recursive simplification to draw the original graph, keeping the pivots. We compare these two algorithms with the state of the art to assess their performances.
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Submitted on : Monday, October 15, 2018 - 3:42:09 PM
Last modification on : Friday, August 21, 2020 - 4:50:13 AM
Long-term archiving on: : Wednesday, January 16, 2019 - 3:13:40 PM


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  • HAL Id : tel-01895891, version 1



Antoine Hinge. Dessin de graphe distribué par modèle de force : application au Big Data. Algorithme et structure de données [cs.DS]. Université de Bordeaux, 2018. Français. ⟨NNT : 2018BORD0092⟩. ⟨tel-01895891⟩



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