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MGV: a system for visualizing massive multidigraphs

机译:MGV:用于可视化大量多重图的系统

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Describes MGV (Massive Graph Visualizer), an integratednvisualization and exploration system for massive multidigraphnnavigation. It adheres to the visual information-seeking mantra:noverview first, zoom and filter, then details on demand. MGV's onlynassumption is that the vertex set of the underlying digraph correspondsnto the set of leaves of a pre-determined tree T. MGV builds annout-of-core graph hierarchy and provides mechanisms to plug in arbitrarynvisual representations for each graph hierarchy slice. Navigation fromnone level to another of the hierarchy corresponds to the implementationnof a drill-down interface. In order to provide the user with navigationncontrol and interactive response, MGV incorporates a number ofnvisualization techniques like interactive pixel-oriented 2D and 3D maps,nstatistical displays, color maps, multi-linked views and a zoomablenlabel-based interface. This makes the association of geographicninformation and graph data very natural. To automate the creation of thenvertex set hierarchy for MGV, we use the notion of graph sketches. Theyncan be thought of as visual indices that guide the navigation of anmultigraph too large to fit on the available display. MGV follows thenclient-server paradigm and it is implemented in C and Java-3D. Wenhighlight the main algorithmic and visualization techniques behind thentools and, along the way, point out several possible applicationnscenarios. Our techniques are being applied to multigraphs defined onnvertex sets with sizes ranging from 100 million to 250 million vertices
机译:描述MGV(大规模图形可视化工具),这是一种用于大规模多图导航的集成可视化和探索系统。它遵循视觉信息搜寻的口头禅:先浏览,缩放和过滤,然后再按需提供详细信息。 MGV的唯一假设是底层有向图的顶点集与预定树T的叶子集相对应。MGV建立了无核图层次结构,并提供了为每个图层次结构切片插入任意视觉表示的机制。从无级导航到层次结构的另一级对应于向下钻取接口的实现。为了向用户提供导航控制和交互式响应,MGV集成了许多可视化技术,例如交互式的面向像素的2D和3D地图,统计显示,彩色地图,多链接视图以及基于zoomablenlabel的界面。这使得地理信息和图形数据的关联非常自然。为了自动为MGV创建thenvertex集层次结构,我们使用了图形草图的概念。可以将Theyn看作是视觉索引,它指导多图的导航太大而无法容纳在可用的显示器上。 MGV遵循了client-server的范例,并以C和Java-3D实现。 Wenhighlight突出了thentools背后的主要算法和可视化技术,并指出了几种可能的应用场景。我们的技术正在应用于定义在1亿到2.5亿个顶点之间的nvertex集上的多图

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