AI 中文总结
研究图采样中原始网络与采样网络的差异，提出系统量化措施，构建DiffLens可视化系统，通过三种视觉设计展示不同类型差异，经案例和用户研究验证其在探索差异与比较策略方面的有效性和可用性。
AI 中文摘要
图采样技术广泛用于简化网络计算和可视化，导致采样网络与原始网络在节点、边和结构上存在不可避免的差异。研究这些差异有助于用户了解不同技术优缺点并选择合适的技术，也有助于开发者评估自身技术，但目前尚无系统方法。本文提出系统通用的量化措施来量化三类图差异，在此基础上进一步提出DiffLens可视化系统，通过三种基于透镜的视觉设计分别展示不同类型差异。通过两个案例研究和一个用户研究评估了DiffLens，结果证实了其在帮助用户探索局部差异和比较不同图采样策略方面的有效性和可用性。
英文摘要
Graph sampling techniques have been widely used to simplify network computation and visualization, which also results in inevitable differences between the sampled networks and the original networks in terms of nodes, edges and structures. Investigating such differences can inform graph sampling technique users of the pros and cons of different techniques and select the appropriate one, and can also help graph sampling developers evaluate their own technique. However, there are still no systematic ways to achieve such a goal. This paper fills this research gap by first proposing systematic and generic quantitative measures to quantify three categories of graph differences (i.e., neighbor-based, path-based, and structure-based). Built upon this, we further propose DiffLens, a novel visualization system to help graph sampling developers and users intuitively explore local differences at different regions of their interest within a sampled graph, where three new lens-based visual designs are presented to display the neighbor-based, path-based, and structure-based differences respectively. We conducted two case studies and a user study using real-world network datasets to evaluate DiffLens. The results confirmed its effectiveness and usability in helping users explore local differences and compare different graph sampling strategies.
