Dynamic graph visualization attracts researchers' concentration as it represents time-varying relationships between entities in multiple domains (e.g., social media analysis, academic cooperation analysis, team sports analysis). Integrating visual analytic methods is consequential in presenting, comparing, and reviewing dynamic graphs. Even though dynamic graph visualization is developed for many years, how to effectively visualize large-scale and time-intensive dynamic graph data with subtle changes is still challenging for researchers. To provide an effective analysis method for this type of dynamic graph data, we propose a snapshot generation algorithm involving Human-In-Loop to help users divide the dynamic graphs into multi-granularity and hierarchical snapshots for further analysis. In addition, we design a visual analysis prototype system (DGSVis) to assist users in accessing the dynamic graph insights effectively. DGSVis integrates a graphical operation interface to help users generate snapshots visually and interactively. It is equipped with the overview and details for visualizing hierarchical snapshots of the dynamic graph data. To illustrate the usability and efficiency of our proposed methods for this type of dynamic graph data, we introduce two case studies based on basketball player networks in a competition. In addition, we conduct an evaluation and receive exciting feedback from experienced visualization experts.
翻译:动态图形可视化吸引研究人员的集中,因为它代表了多个领域(如社交媒体分析、学术合作分析、团队体育分析等)实体之间的时间差异关系; 整合视觉分析方法在展示、比较和审查动态图表方面产生了影响。 尽管动态图形可视化已经开发多年,但如何有效地直观化大型和时间密集动态图表数据,并细微变化对研究人员来说仍然具有挑战性。 为提供这类动态图形数据的有效分析方法,我们建议使用由人类-内卢普参与的即时生成算法,帮助用户将动态图表分为多色化和等级缩略图,以供进一步分析。此外,我们设计了一个视觉分析原型系统(DGS Vision),以协助用户有效地获取动态图形洞察力。DGS Vision综合了一个图形操作界面,以帮助用户生成视觉和互动的图片。它配备了动态图形数据可视化等级缩图的概览和细节。为了说明我们为这种动态图表数据而提出的方法的实用性和有效性和效率,我们从一个视觉竞争中的篮球播放者网络进行两项案例研究。