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Wavelet-based visualization of time-varying data on graphs

机译：基于小波的基于时变数据的可视化

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Visualizing time-varying data defined on the nodes of a graph is a challenging problem that has been faced with different approaches. Although techniques based on aggregation, topology, and topic modeling have proven their usefulness, the visual analysis of smooth and/or abrupt data variations as well as the evolution of such variations over time are aspects not properly tackled by existing methods. In this work we propose a novel visualization methodology that relies on graph wavelet theory and stacked graph metaphor to enable the visual analysis of time-varying data defined on the nodes of a graph. The proposed method is able to identify regions where data presents abrupt and mild spacial and/or temporal variation while still been able to show how such changes evolve over time, making the identification of events an easier task. The usefulness of our approach is shown through a set of results using synthetic as well as a real data set involving taxi trips in downtown Manhattan. The methodology was able to reveal interesting phenomena and events such as the identification of specific locations with abrupt variation in the number of taxi pickups.
机译：可视化图形节点上定义的时变数据是一个具有挑战性的问题，已经面临着不同的方法。尽管基于聚合，拓扑和主题建模的技术已经证明了它们的有用性，但是对平滑和/或突然数据变化的视觉分析以及随着时间的推移随着时间的变化的演变是未被现有方法正确解决的方面。在这项工作中，我们提出了一种依赖于图形小波理论和堆叠图隐喻的新颖可视化方法，以实现在图形节点上定义的时变数据的视觉分析。所提出的方法能够识别数据呈现突然和温和的间隔和/或时间变化的区域，同时仍然能够展示这种变化随时间的发展方式，使得识别事件是更容易的任务。我们的方法的有用性通过一系列的结果，使用综合性以及涉及曼哈顿市中心的出租车旅行的真实数据集。该方法能够揭示有趣的现象和事件，例如识别出租车拾取数量的突然变化。

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