AI 中文总结
VisPuzzle是一种任务感知的可视化组合方法，将其建模为组合图上的逐步搜索问题，结合蒙特卡洛图搜索与多维度奖励函数生成高质量可视化组合，经用例与用户研究验证其有效性。
AI 中文摘要
将多个可视化组合成一个连贯的整体仍然具有挑战性，因为设计空间庞大，且需要平衡对任务相关数据洞察（如趋势和异常值）的覆盖、感知清晰度和美学质量。在本文中，我们提出VisPuzzle，一种任务感知方法，将可视化组合表述为组合图上的逐步搜索问题。在该图中，节点表示数据组合操作（如并集、连接）或确定组件关系、空间排列或组件比例的视觉组合操作，边编码操作之间的可行转换。我们采用蒙特卡洛图搜索，在奖励函数（平衡任务相关性、感知有效性和美学连贯性）的引导下，从该图中高效识别高质量的组合候选。一个用例和用户研究表明，VisPuzzle生成的排名最高的候选与人类对组合质量的判断高度一致，证明了其在支持有原则且可扩展的可视化组合方面的实用性。
英文摘要
Compositing multiple visualizations into a coherent whole remains challenging due to the vast design space and the need to balance the coverage of task-relevant data insights (e.g., trends and outliers), perceptual clarity, and aesthetic quality. In this paper, we present VisPuzzle, a task-aware method that formulates visualization composition as a stepwise search problem over a composition graph. In this graph, nodes represent either data composition operations (e.g., union, join) or visual composition operations that determine component relationships, spatial arrangements, or component proportions, and edges encode feasible transitions between operations. We employ Monte Carlo Graph Search to efficiently identify high-quality composition candidates from this graph, guided by a reward function that balances task relevance, perceptual effectiveness, and aesthetic coherence. A use case and a user study show that the top-ranked candidates produced by VisPuzzle align closely with human judgments of composition quality, demonstrating its utility in supporting principled and scalable visualization composition.
CommentsAccepted by IEEE VIS 2026
