Visualisation stéréoscopique et interactive de structures en communautés dans des graphes

Abstract : Ever since the pioneering work of Moreno in 1934, social network analysis has always included drawings depicting relationships between actors. From these days, the graph visualization field has grown within the graph drawing and information visualization communities. Besides the algorithmic and combinatoric questions arising from graph drawings, new challenges now include networks visual data mining. Usually referred to as visual analytics it involves the integration of the user at the heart of the analysis. In this thesis, we focus on interactive and stereoscopic visual restitutions allowing the user to drive the mining process. Using a ad hoc experimental environment, we try to assert their its impact on a popular task of community detection. Through several experiments, we show that for a specific class of graphs, 2D seems more adapted for the easier graphs while stereoscopic 3D is beneficial for the more complex ones. We also identify some differences in the interactions between the stereo and mono conditions, which seems to indicate behavioral differences emerging from differing interaction strategies. We also propose some prospects such as the implementation of a library allowing hands-free interactions adapted to visual mining in front of a large screen.
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Nicolas Greffard. Visualisation stéréoscopique et interactive de structures en communautés dans des graphes. Interface homme-machine [cs.HC]. Université de Nantes, 2013. Français. ⟨tel-01072368⟩

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