Exploring the Limits of Complexity: A Survey of Empirical Studies on Graph Visualisation
Exploring the Limits of Complexity: A Survey of Empirical Studies on Graph Visualisation
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DOI：
10.1016/j.visinf.2018.12.006
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Vahan Yoghourdjian;D. Archambault;S. Diehl;Tim Dwyer;Karsten Klein;H. Purchase;Hsiang-Yun Wu
中科院分区：
文献类型：
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作者：
Vahan Yoghourdjian;D. Archambault;S. Diehl;Tim Dwyer;Karsten Klein;H. Purchase;Hsiang-Yun Wu
几十年来，信息可视化和图形绘制领域的研究人员一直致力于开发大型复杂网络的布局和显示技术。涉及人类参与者的实验也探索了这种网络的不同风格的布局和表示的可读性。在这两种文献中，网络经常被称为“大型”或“复杂”，但这些术语都是相对的。从以人为中心的实验角度来看，什么构成“大”（例如）取决于几个因素，例如数据复杂性、视觉复杂性和所使用的技术。在本文中，我们回顾了以人为中心的实验文献，以了解在实践中，节点链接图的不同特征和特征如何影响视觉复杂性。
For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being ‘large’ or ‘complex’, yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes ‘large’ (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node–link diagrams affect visual complexity.