Author: paul.rosen

We have identified three properties for faithfully representing Reeb graphs in a visualization. Namely, they should be constrained to the boundary, compact, and aligned with the function gradient. Existing algorithms for drawing Reeb graphs are agnostic to or violate these properties. In this paper, we introduce an algorithm to generate Reeb graph visualizations, called GASP, that is cognizant of these properties, thereby producing visualizations that are more representative of the underlying data.

We present Buckaroo, a visualization system to highlight discrepancies in data and enable on-the-spot corrections through direct manipulations of visual objects. Buckaroo (1) automatically finds “interesting” data groups that exhibit anomalies compared to the rest of the groups and recommends them for inspection; (2) suggests wrangling actions that the user can choose to repair the anomalies; and (3) allows users to visually manipulate their data by displaying the effects of their wrangling actions and offering the ability to undo or redo these actions, which supports the iterative nature of data wrangling.

We propose a declarative extension to Wilkinson’s Grammar of Graphics that reifies annotations as first-class design elements, enabling structured specification of annotation targets, types, and positioning strategies. Through comparison with eight existing tools, we show that our approach enhances expressiveness, reduces authoring effort, and enables portable, semantically integrated annotation workflows.

We present a comprehensive survey of 191 research papers describing empirical studies, tools, techniques, and systems. We characterize annotations by their types, generation methods, and targets, and examine their use across four primary application domains: user engagement, storytelling, collaboration, and exploratory data analysis, and we discuss key trends, practical challenges, and open research directions. These findings offer a foundation for designing more effective annotation systems and advancing future research on annotation in visualization.

We introduce a framework highlighting five key perceptual mechanisms used in node-link diagrams and adjacency matrices: attention, visual search, perceptual organization, ensemble coding, and object recognition. Our framework describes the role these perceptual mechanisms play in common network analytical tasks.