Zhang Dongping, Adar Eytan, Hullman Jessica
IEEE Trans Vis Comput Graph. 2022 Jan;28(1):443-453. doi: 10.1109/TVCG.2021.3114679. Epub 2021 Dec 24.
Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.
使用传统的节点链接图来可视化概率图具有挑战性。使用诸如宽度或模糊度等视觉变量对边的概率进行编码，会使静态网络可视化的用户难以在不确定性下估计网络统计量，如密度、孤立点、路径长度或聚类系数。我们引入了网络假设结果图（NetHOPs），这是一种可视化技术，它能对从由概率边定义的网络分布中采样得到的一系列网络实现进行动画展示。NetHOPs采用了动态和纵向图绘制中使用的聚合和锚定算法，为不确定性估计参数化布局稳定性。我们提出了一种社区匹配算法，以实现对聚类成员和社区出现的不确定性进行可视化。我们描述了一项研究的结果，在该研究中，51位网络专家使用NetHOPs完成了一组常见的视觉分析任务，并报告了他们如何感知受不确定性影响的网络结构和属性。参与者的估计平均落在真实统计量的11%以内，这表明NetHOPs可能是一种合理的方法，能让网络分析师在不确定性下对多种属性进行推理。当参与者能够操纵布局锚定和动画速度时，他们似乎能更准确地阐述网络统计量的分布。基于这些发现，我们综合了关于开发和使用概率网络动画可视化的设计建议。
