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Generating Coherent Visualization Sequences for Multivariate Data by Causal Graph Traversal
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AI
DOI：10.1109/TVCG.2026.3656952.png)
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Abstract
En 中文
Multivariate data contain an abundance of information and many techniques have been proposed to allow humans to navigate this information in an ordered fashion. For this work, we focus on methods that seek to convey multivariate data as a collection of bivariate scatterplots or parallel coordinates plots. Presenting multivariate data in this way requires a regime that determines in what order the bivariate scatterplots are presented or in what order the parallel coordinate axes are arranged. We refer to this order as a visualization sequence. Common techniques utilize standard statistical metrics like correlation, similarity or consistency. We expand on the family of statistical metrics by incorporating the rigidity of causal relationships. To capture these relationships, we first derive a causal graph from the data and then allow users to select from several semantic traversal schemes to derive the respective chart sequence. We tested the sequences with a crowd-sourced user study and a user interview to confirm that the causality-informed visualization sequences help viewers to better grasp the causal relationships that exist in the data, as opposed to sequences derived from correlations or randomization alone.
Keywords:
Causality
causal graph
visualization sequence
multivariate visualization
parallel coordinates
Journal
IF:
6.5
Papers:
309
Citations:
2.2W