arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.07944cs.HCcs.GR
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认知负荷视角下的网络可视化Scalability

Scalability of Network Visualisation from a Cognitive Load Perspective

Vahan Yoghourdjian, Yalong Yang, Tim Dwyer, Lee Lawrence, Michael Wybrow, Kim Marriott

备注：
To be presented at IEEE Conference on Information Visualization (InfoVis 2020)
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摘要：

节点链接图被广泛用于可视化网络。然而，即使是最好的网络布局算法，当图形达到一定程度的复杂性时，最终也会导致“毛球”可视化，需要通过聚合或交互（如过滤）进行简化以保持可用。到目前为止，几乎没有数据表明节点链接图在什么复杂程度上变得无效，或者视觉复杂性如何影响认知负荷。为此，我们进行了一项对照研究，以了解需要详细了解网络拓扑的任务的工作量限制-找到两个节点之间的最短路径。我们在具有25到175个节点的不同密度的图上测试了性能。我们收集了性能指标（准确性和响应时间）、主观反馈和生理指标（EEG、瞳孔扩张和心率变异性）。据我们所知，这是第一项包含生理测量的网络可视化研究。我们的研究结果表明，人们有显着的困难，找到最短路径的高密度节点链接图超过50个节点，甚至低密度图超过100个节点。从我们收集的EEG数据中，我们观察到了困难和简单任务之间大脑活动的功能差异。我们发现，认知负荷增加到一定的难度水平后，它下降，可能是因为参与者放弃了。我们还探讨了全局网络布局特征（如交叉点的大小或数量）和最短路径特征（如长度或直线度）对任务难度的影响。我们发现，全局特征通常比最短路径的特征具有更大的影响。

英文摘要：

Node-link diagrams are widely used to visualise networks. However, even the best network layout algorithms ultimately result in 'hairball' visualisations when the graph reaches a certain degree of complexity, requiring simplification through aggregation or interaction (such as filtering) to remain usable. Until now, there has been little data to indicate at what level of complexity node-link diagrams become ineffective or how visual complexity affects cognitive load. To this end, we conducted a controlled study to understand workload limits for a task that requires a detailed understanding of the network topology---finding the shortest path between two nodes. We tested performance on graphs with 25 to 175 nodes with varying density. We collected performance measures (accuracy and response time), subjective feedback, and physiological measures (EEG, pupil dilation, and heart rate variability). To the best of our knowledge, this is the first network visualisation study to include physiological measures. Our results show that people have significant difficulty finding the shortest path in high-density node-link diagrams with more than 50 nodes and even low-density graphs with more than 100 nodes. From our collected EEG data we observe functional differences in brain activity between hard and easy tasks. We found that cognitive load increased up to a certain level of difficulty after which it decreased, likely because participants had given up. We also explored the effects of global network layout features such as size or number of crossings, and features of the shortest path such as length or straightness on task difficulty. We found that global features generally had a greater impact than those of the shortest path.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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