Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation
Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation
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DOI：
10.1109/tvcg.2014.2346422
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发表时间：
2014-12-01
影响因子：
5.2
通讯作者：
Boerner, Katy
中科院分区：
文献类型：
--
作者：
Saket, Bahador;Simonetto, Paolo;Boerner, Katy
有效地显示数据集中对象之间的关系是信息可视化的主要任务之一。通常存在对象对之间的距离的明确定义的概念，并且使用诸如主成分分析或多维缩放的传统方法来将对象放置为2D空间中的点，从而使得相似的对象彼此靠近。在另一个典型的背景下。数据集被可视化为网络图，其中相关节点通过链接连接。最近，数据集也被可视化为地图，其中除了节点和链接之外，还有组和集群的显式表示。我们考虑这三种技术，其特征是编码信息量的逐渐增加：节点图。节点-链接图和节点-链接组图。我们通过一个对照实验来评估这三种类型的图表，该实验涵盖了九种不同的任务，大致分为三类：基于节点的任务、基于网络的任务和基于组的任务。我们的发现表明，添加链接或链接和组表示不会对基于节点的任务的性能(时间和准确性)产生负面影响。同样，添加组表示不会对基于网络的任务的性能产生负面影响。节点-链接-组图在基于组的任务中表现优于其他图。这些结论与其他研究的结果相矛盾，这些研究的背景相似，但略有不同。然而，综合来看，这些结果可能会对标准和领域特定的可视化工具的设计产生重大影响。
Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting. the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams. node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain specific visualizations tools.