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Hyper-bag-graphs and their applications: Modeling, Analyzing and Visualizing Complex Networks of Co-occurrences

Le Goff, Jean Marie Alian F.
Defense Thèse de doctorat : Univ. Genève, 2020 - Sc. 5449 - 2020/03/13
Abstract Big Data calls for techniques to gain insight into the tremendous amount of data generated. This Thesis proposes a systematic approach to model using families of multisets, called hb-graphs, analyse and visualize complex co-occurrence networks, usually modelled by (hyper)graphs. Retrieving important information calls for coarsening: diffusion fits for networks and potentially requires a Laplacian tensor linked to an adjacency tensor. Revisiting systematically how diffusion can be achieved on hb-graphs, using firstly the incident matrix, which gives a baseline for the evaluation of the m-uniformisation process required for building an e-adjacency tensor for general hb-graphs, showing that any such process has an influence on the exchange-based diffusion itself; in order to improve this a layered Laplacian tensor is proposed. Two applications are then tackled, including a hb-graph framework to visually query an information space and a proposal to aggregate the rankings of reference between the different facets using the exchange-based diffusion.
Keywords Hyper-bag-graphHb-graphE-adjacency tensorLaplacian tensorHypergraphCo-occurrence networkExchange-based diffusionInformation space
URN: urn:nbn:ch:unige-1375209
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Thesis (16.7 MB) - public document Free access
Research group Computer Vision and Multimedia Laboratory
Project CERN Collaboration Spotting
(ISO format)
OUVRARD, Xavier Eric. Hyper-bag-graphs and their applications: Modeling, Analyzing and Visualizing Complex Networks of Co-occurrences. Université de Genève. Thèse, 2020. doi: 10.13097/archive-ouverte/unige:137520 https://archive-ouverte.unige.ch/unige:137520

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Deposited on : 2020-06-22

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