AI 中文总结
该研究针对Munzner可视化设计模型未充分关注算法视角的问题，提出用清晰算法视角模型补充设计视角，以分离设计与算法关注点，提升可视化研究的可信度与理解深度。
AI 中文摘要
信息可视化本质上是融合多个研究领域的学科，大致可分为两种视角：一是设计视角，围绕如何确保人类能有效处理数据的视觉表示及相关工具；二是算法视角，聚焦如何自动生成这类视觉表示。Munzner的可视化设计模型将设计选择置于算法考量之前，主要体现设计视角，导致该模型的应用常将算法视角视为事后补充，跳过了将设计转化为算法研究所需形式化表达的步骤，进而使设计与计算可视化所用算法纠缠在一起。我们以布局算法为研究对象，探究这种纠缠的影响：质量常未被定义和衡量，且倾向采用临时启发式方法，降低了可信度，可能导致错误结论。我们研究如何用清晰的算法视角模型补充Munzner模型的设计视角，以获得对可视化及其生成算法间相互作用的形式化、可衡量的理解。矛盾的是，解决方案在于更清晰地分离设计与算法的关注点。我们认为该模型能更好地对比不同方法、更细致地理解其优缺点，还能发现新机遇，最终促进对可视化的更好理解。
英文摘要
Information visualization is inherently a field that brings together various research domains. Roughly speaking, we may identify two perspectives: the design perspective, revolving around how to ensure that a human can work effectively with the visual representations of data and the tools that offer them, and the algorithmic perspective, focusing on how to automatically create such visual representations. Munzner's model for visualization design places design choices before algorithmic considerations. It offers predominantly a design perspective; as a consequence, applications of this model may consider the algorithmic perspective as an afterthought, bypassing a step that translates the design into the formalism necessary for algorithmic study. As a result, the design may be entangled with the algorithms used to compute a visualization. Focusing on layout algorithms, we explore the ramifications of this entanglement: quality often goes undefined and unmeasured, and ad-hoc heuristics tend to be applied, reducing trustworthiness and potentially leading to incorrect conclusions. We look at how we may complement Munzner's model---the design perspective---with a clear model of the algorithmic perspective, to obtain a formal, measured understanding of the interplay between visualizations and the algorithms used to create them. Paradoxically, the solution lies in a clearer separation of concerns between design and algorithm. We argue that this model leads to better comparison between approaches, a more fine-grained understanding of their strengths and weaknesses, and allows for uncovering new opportunities, as to eventually lead to a better understanding of visualization.
CommentsAccepted for presentation at IEEE VIS 2026 and to appear in IEEE TVCG in 2027
