Evaluating Perceptually Complementary Views for Network Exploration Tasks
Evaluating Perceptually Complementary Views for Network Exploration Tasks
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DOI：
10.1145/3025453.3026024
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发表时间：
2017-05
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影响因子：
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通讯作者：
Chunlei Chang;Benjamin Bach;Tim Dwyer;K. Marriott
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文献类型：
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作者：
Chunlei Chang;Benjamin Bach;Tim Dwyer;K. Marriott
我们通过三个对照研究探讨了矩阵、节点链接和组合并排视图在加权网络可视化中的相对优点：(1)在矩阵表示中找到加权边的最有效的视觉编码；(2)比较静态加权网络的矩阵视图、节点链接视图和组合视图；(3)比较MatrixWave、Sankey以及两者的组合视图对事件序列数据的影响。我们的研究强调节点链接和矩阵视图适用于不同的分析任务。对于合并的观点，我们的研究表明，在提高某些任务的准确性方面存在感知互补效应，但在完成时间方面比单独使用两种技术更快有成本。眼球运动数据显示，在许多任务中，参与者在训练阶段尝试了两种观点后，强烈倾向于两种观点中的一种。
We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.