Rapid, Progressive Sub-Graph Explorations for Interactive Visual Analytics over Large-Scale Graph Datasets
Rapid, Progressive Sub-Graph Explorations for Interactive Visual Analytics over Large-Scale Graph Datasets
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DOI：
10.1145/3365109.3368793
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Samuel Armstrong;Kevin Bruhwiler;S. Pallickara
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文献类型：
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作者：
Samuel Armstrong;Kevin Bruhwiler;S. Pallickara
可视化地探索海量的图形数据集是一项具有挑战性的任务，因为在导航过程中存在大量的数据和缺乏可依赖的结构。Indra是我们的大规模图表数据框架，可为大规模图表数据集提供响应性的视觉分析。在这项研究中，我们提出了一种新的图索引方案，该方案将图的视图旋转到层次结构中，同时在用户的分析场景中保持顶点的语义重要性。通过支持链接的多视图和多分辨率操作，如钻取和汇总，Indra允许用户比较和跟踪子图形的多个方面。我们已经执行了一组分析Indra的经验基准测试，这些测试表明，几个操作的执行延迟为亚秒级，从而有效地支持交互式视觉分析。
Exploring a voluminous graph dataset visually is a challenging task due to the sheer amount of data and the lack of structure to rely on during the navigation. Indra, our framework for large-scale graph data, provides responsive visual analytics over large-scale graph datasets. In this study, we propose a novel graph indexing scheme that pivots the view of the graph to a hierarchical structure while preserving the semantic importance of vertices within the user's analysis scenario. Indra allows users to compare and track multiple aspects of sub-graphs by supporting linked multi-views and multi-resolution operations such as drill-in and roll-ups. We have performed a set of empirical benchmarks profiling Indra and these demonstrate that several operations are executed with sub-second latency to effectively support interactive visual analytics.