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Toward automatic comparison of visualization techniques: Application to graph visualization

机译：朝着可视化技术的自动比较：应用于图形可视化

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Many end-user evaluations of data visualization techniques have been run during the last decades. Their results are cornerstones to build efficient visualization systems. However, designing such an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility. We believe that modern and efficient computer vision techniques, such as deep convolutional neural networks (CNNs), may help visualization researchers to build and/or adjust their evaluation hypothesis. The basis of our idea is to train machine learning models on several visualization techniques to solve a specific task. Our assumption is that it is possible to compare the efficiency of visualization techniques based on the performance of their corresponding model. As current machine learning models are not able to strictly reflect human capabilities, including their imperfections, such results should be interpreted with caution. However, we think that using machine learning-based pre-evaluation, as a pre-process of standard user evaluations, should help researchers to perform a more exhaustive study of their design space. Thus, it should improve their final user evaluation by providing it better test cases. In this paper, we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be. That study compares two mainstream graph visualization techniques: node-link (NL) and adjacency-matrix (AM) diagrams. Using two well-known deep convolutional neural networks, we partially reproduced user evaluations from Ghoniemet al.and from Okoeet al.. These experiments showed that some user evaluation results can be reproduced automatically.
机译：在过去几十年中，许多最终用户评估已经运行了数据可视化技术。它们的结果是建立有效的可视化系统的基石。然而，设计这样的评估始终复杂且耗时，并且可能缺乏统计证据和再现性。我们认为，现代和高效的计算机视觉技术，如深卷积神经网络（CNNS），可以帮助可视化研究人员建立和/或调整他们的评估假说。我们的想法的基础是在几种可视化技术上训练机器学习模型来解决特定任务。我们的假设是可以基于它们对应模型的性能进行比较可视化技术的效率。由于目前的机器学习模型无法严格反映人类能力，包括他们的缺陷，这些结果应谨慎解释。然而，我们认为，使用基于机器学习的预评估作为标准用户评估的预处理，应该帮助研究人员对其设计空间进行更详尽的研究。因此，它应该通过提供更好的测试用例来改善他们的最终用户评估。在本文中，我们介绍了我们进行的两个实验的结果，以评估如何相关的用户和计算机视觉技术的相关性。该研究比较了两个主流图形可视化技术：节点链路（NL）和邻接矩阵（AM）图。使用两个众所周知的深度卷积神经网络，我们部分地从Ghoniemet Al.and中重现了用户评估。这些实验表明，可以自动再现一些用户评估结果。

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