IEEE VIS 2024 Content: Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

Xinhuan Shu - Newcastle University, Newcastle Upon Tyne, United Kingdom. University of Edinburgh, Edinburgh, United Kingdom

Alexis Pister - University of Edinburgh, Edinburgh, United Kingdom

Junxiu Tang - Zhejiang University, Hangzhou, China

Fanny Chevalier - University of Toronto, Toronto, Canada

Benjamin Bach - Inria, Bordeaux, France. University of Edinburgh, Edinburgh, United Kingdom

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Room: Bayshore VII

2024-10-18T12:54:00ZGMT-0600Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
We propose Pattern Explainer to help analysts who are unfamiliar with network visualizations learn about visual patterns in the representation of their data. Looking at the visualization, a user spots a visual pattern of interest, e.g. a “bug”-looking pattern in the matrix. To inquire about whether this pattern is meaningful, the user selects the area. Pattern Explainer then automatically mines the selection, against a dictionary of network motifs, and provides the user with explanations of what underlying network patterns the visual pattern reveals.
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Keywords

Visualization education, network visualization

Abstract

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer’s selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology.