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用于网络演化分析的任务分类法。

A task taxonomy for network evolution analysis.

作者信息

Ahn Jae-wook, Plaisant Catherine, Shneiderman Ben

机构信息

University of Maryland, College Park.

出版信息

IEEE Trans Vis Comput Graph. 2014 Mar;20(3):365-76. doi: 10.1109/TVCG.2013.238.

Abstract

Visualization has proven to be a useful tool for understanding network structures. Yet the dynamic nature of social media networks requires powerful visualization techniques that go beyond static network diagrams. To provide strong temporal network visualization tools, designers need to understand what tasks the users have to accomplish. This paper describes a taxonomy of temporal network visualization tasks. We identify the 1) entities, 2) properties, and 3) temporal features, which were extracted by surveying 53 existing temporal network visualization systems. By building and examining the task taxonomy, we report which tasks are well covered by existing systems and make suggestions for designing future visualization tools. The feedback from 12 network analysts helped refine the taxonomy.

摘要

可视化已被证明是理解网络结构的有用工具。然而,社交媒体网络的动态特性需要超越静态网络图的强大可视化技术。为了提供强大的时态网络可视化工具,设计师需要了解用户必须完成的任务。本文描述了时态网络可视化任务的分类法。我们通过调查53个现有的时态网络可视化系统,确定了1)实体、2)属性和3)时态特征。通过构建和研究任务分类法,我们报告了现有系统对哪些任务覆盖良好,并为设计未来的可视化工具提出建议。12位网络分析师的反馈有助于完善该分类法。

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