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

TL;DR

This work introduces Pattern Explainer, an interactive in-situ learning tool for network visualizations that lets analysts select a region to retrieve and understand underlying network motifs and corresponding visual patterns. It situates Pattern Explainer within visualization literacy and contrasts it with cheat sheets and text-only explanations through qualitative and quantitative studies involving 32 participants, demonstrating improved pattern recognition and terminology learning. The paper defines a pattern dictionary, detection heuristics, and supports three visualization types (node-link, adjacency matrices, time-arcs), highlighting practical implications for visualization onboarding and literacy. It also discusses limitations, potential extensions, and longer-term research directions toward a broader theory of patterns in 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.

Figures (5)

• Figure 1: Network visualizations considered in this work and their particular designs. The solid rectangles show some portions participants annotated, while the text around was added by the authors for clarity in the paper, not visible to the participants. The dashed rectangles show some visual patterns the authors annotated. The designs in these visualizations are optimized by the authors of this paper for readability. (a) and (c) use the Marie Boucher Trade network dataset marieBoucher, and (b) uses the 'Les Misérables' Co-occurrence network dataset netRep.
• Figure 2: The Pattern Explainer idea. (a) In the bottom-up explanation, a user can select an arbitrary region of interest in a network visualization. Our system retrieves all the underlying network patterns in the user selection, backed by a pattern repository and a set of heuristics, and pops up an overview. After a user selects a pattern for exploration, the pop-up provides visual-textual explanations of the network and visual patterns, and lists other instances for browsing. (b) In the top-down explanation, the user can browse all the found instances according to pattern types.
• Figure 3: An overview of the pattern repository (a, b). Network patterns (black background) are organized vertically, while corresponding visual patterns (white background) are listed horizontally in each of the three visualizations. Icons for visual patterns include one lead icon (gray background) and several smaller versions of visual variations. (c) illustrates temporal variations of a clique pattern in time-arcs that were excluded in the repository.
• Figure 4: Explainer pop-up: Example of selections and generated pop-ups for the fan motif in (a) a matrix and (b) a time-arcs visualization.
• Figure 5: Quantitative study results. (a) shows the number of patterns correctly identified with the distribution as well as the number of patterns reported in total. (b) shows confidence after training and after testing. Error bars indicate 95% confidence intervals.