通过采样海量序列视图可视化动态网络

Ying Zhao, Wenjiang Chen, Yanmin She, Qing Wu, Yanni Peng, Xiaoping Fan
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引用次数: 4

摘要

海量序列视图(MSV)是一种重要的基于时间线的动态网络可视化技术。然而,当有限的屏幕空间容纳过多的网络边缘时,它经常遭受严重的视觉混乱。受静态图分析中使用图采样的启发,我们提出利用图采样来减少MSV中的视觉杂波。为了通过MSV实现动态网络的可视化,设计了一种基于接受-拒绝随机抽样的边缘采样方法。该方法能够在保持时变网络行为的同时,提高MSV的整体可读性。这也是将图采样技术应用于动态网络分析的初步尝试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing Dynamic Network via Sampled Massive Sequence View
Massive Sequence View(MSV) is an important timeline-based technique for dynamic network visualization. However, it often suffers from severe visual clutter when limited screen space holds excessive network edges. Inspired by the use of graph sampling in static graph analysis, we propose to utilize graph sampling to reduce visual clutter in MSV. An edge sampling method based on accept-reject random sampling is designed for visualizing dynamic network via MSV. The method is able to improve the overall readability of MSV while preserving time varying network behaviors. It is also a preliminary attempt to apply graph-sampling technique into dynamic network analysis.
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