Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach
Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach
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DOI：
10.1145/3313831.3376381
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发表时间：
2020-01
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影响因子：
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通讯作者：
C. Nobre;Dylan Wootton;Lane Harrison;A. Lex
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文献类型：
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作者：
C. Nobre;Dylan Wootton;Lane Harrison;A. Lex
可视化多元网络是具有挑战性的，因为有效地编码网络拓扑并编码与节点和边缘相关的属性所需的权衡。存在大量的多元网络可视化技术，但几乎没有关于它们各自的优势和缺点的经验指导。在本文中，我们描述了一个众包实验，将节点 - 链接图与与并列表并列表的鼻链路编码和邻接矩阵进行了比较。我们发现节点链接图最适合需要在网络拓扑和一些属性之间进行密切集成的任务。邻接矩阵对于与集群有关的任务以及需要考虑许多属性的任务表现良好。我们还反思了使用经过验证的设计在众包环境中使用经验评估复杂的，交互式可视化的方法。我们强调了培训，薪酬和出处跟踪的重要性。
Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking.