GRAPHTRIALS: Visual Proofs of Graph Properties.

IF 6.5
Henry Forster, Felix Klesen, Tim Dwyer, Peter Eades, Seok-Hee Hong, Stephen Kobourov, Giuseppe Liotta, Kazuo Misue, Fabrizio Montecchiani, Alexander Pastukhov, Falk Schreiber
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Abstract

Graph and network visualization supports exploration, analysis and communication of relational data arising in many domains: from biological and social networks, to transportation and powergrid systems. With the arrival of AI based question-answering tools, issues of trustworthiness and explainability of generated answers motivate a significant new role for visualization. In the context of graphs, we see the need for visualizations that can convince a critical audience that an assertion (e. g., from an AI) about the graph under analysis is valid. The requirements for such representations that convey precisely one specific graph property are quite different from standard network visualization criteria which optimize general aesthetics and readability. In this paper, we aim to provide a comprehensive introduction to visual proofs of graph properties and a foundation for further research in the area. We present a framework that defines what it means to visually prove a graph property. In the process, we introduce the notion of a visual certificate, that is, a specialized faithful graph visualization that leverages the viewer's perception, in particular, pre-attentive processing (e. g., via pop-out effects), verify to a given assertion about the represented graph. We also discuss the relationships between visual complexity, cognitive load and complexity theory, and propose a classification based on visual proof complexity. Then, we provide further examples of visual certificates for problems in different visual proof complexity classes. Finally, we conclude the paper with a discussion of the limitations of our model and some open problems.

GRAPHTRIALS:图形属性的视觉证明。
图形和网络可视化支持在许多领域中出现的关系数据的探索、分析和交流:从生物和社会网络,到交通和电网系统。随着基于人工智能的问答工具的出现,生成答案的可信度和可解释性问题激发了可视化的重要新角色。在图形的背景下,我们看到了可视化的需求,它可以说服关键的受众,关于正在分析的图形的断言(例如,来自AI)是有效的。这种精确传达一个特定图形属性的表示的要求与优化一般美学和可读性的标准网络可视化标准有很大不同。在本文中,我们的目标是提供一个全面的介绍图形属性的视觉证明和进一步研究该领域的基础。我们提出了一个框架,它定义了可视化证明图属性的意义。在这个过程中,我们引入了视觉证书的概念,即一种专门的忠实图形可视化,它利用了观看者的感知,特别是预先注意的处理(例如,通过弹出效果),验证了关于所表示图形的给定断言。讨论了视觉复杂性、认知负荷和复杂性理论之间的关系,并提出了一种基于视觉证明复杂性的分类方法。然后,我们为不同的可视化证明复杂性类中的问题提供进一步的可视化证书示例。最后,我们讨论了模型的局限性和一些有待解决的问题。
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