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Effective temporal graph layout:A comparative study of animation versus static display methods

机译：有效的时间图布局：动画与静态显示方法的比较研究

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Graph drawing algorithms have classically addressed the layout of static graphs. However, the need to draw evolving or dynamic graphs has brought into question many of the assumptions, conventions and layout methods designed to date. For example, social scientists studying evolving social networks have created a demand for visual representations of graphs changing over time. Two common approaches to represent temporal information in graphs include animation of the network and use of static snapshots of the network at different points in time. Here, we report on two experiments, one in a laboratory environment and another using an asynchronous remote web-based platform, Mechanical Turk, to compare the efficiency of animated displays versus static displays. Four tasks are studied with each visual representation, where two characterise overview level information presentation, and two characterise micro level analytical tasks. For the tasks studied in these experiments and within the limits of the experimental system, the results of this study indicate that static representations are generally more effective particularly in terms of time performance, when compared to fully animated movie representations of dynamic networks.
机译：图形绘制算法经典地解决了静态图形的布局问题。但是，绘制演化图或动态图的需求使迄今设计的许多假设，约定和布局方法受到质疑。例如，研究不断发展的社交网络的社会科学家提出了对图形随时间变化的视觉表示的需求。在图中表示时间信息的两种常用方法包括网络动画和在不同时间点使用网络静态快照。在这里，我们报告了两个实验，一个是在实验室环境中，另一个是使用基于异步远程基于Web的平台Mechanical Turk，以比较动画显示和静态显示的效率。每个视觉表示都研究了四个任务，其中两个表征概述级别的信息表示，两个表征微观级别的分析任务。对于这些实验中研究的任务以及在实验系统范围内的研究，本研究结果表明，与动态网络的全动画电影表示相比，静态表示通常更有效，尤其是在时间表现方面。

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