Temporal networks are widely used to map phenomena into complex systems in several research disciplines, such as computer science, business, and biology. Several layouts can be used in visual analyses of temporal networks. The identification of the most suitable for a given task is, however, not trivial. This paper presents a user study that analyzes the performance of four different layouts: Massive Sequence View (MSV), Temporal Activity Map, matrix animation, and structural animation, when applied to pattern detection tasks of time-evolving networks. Our results show that all four layouts are appropriate to perform the evaluated tasks; however, the structural animation and MSV scored higher across different types of users.

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中文翻译：

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可视化联系网络时间演变的比较分析：用户研究

时间网络被广泛用于将现象映射到多个研究学科的复杂系统中，例如计算机科学、商业和生物学。几种布局可用于时间网络的视觉分析。然而，确定最适合给定任务的方法并非易事。本文介绍了一项用户研究，该研究分析了四种不同布局的性能：大规模序列视图 (MSV)、时间活动图、矩阵动画和结构动画，当应用于时间演化网络的模式检测任务时。我们的结果表明，所有四种布局都适合执行评估任务；然而，结构动画和 MSV 在不同类型的用户中得分更高。

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