Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings

TL;DR

This study investigates user-preferred viewpoints in 3D graph visualizations within virtual reality, identifying key aesthetic and structural factors influencing viewpoint choice and providing a dataset for future research.

Contribution

It presents the first systematic analysis of user preferences for 3D graph viewpoints, introducing new measures and a dataset to guide effective visualization design.

Findings

Stress, Crossings, and Node-Node Overlap are key preference indicators.

User preferences align with classical and 3D-specific aesthetic criteria.

A new measure, Isometric Viewpoint Deviation, effectively captures viewpoint perceivability.

Abstract

The visual analysis of graphs in 3D has become increasingly popular, accelerated by the rise of immersive technology, such as augmented and virtual reality. Unlike 2D drawings, 3D graph layouts are highly viewpoint-dependent, making perspective selection critical for revealing structural and relational patterns. Despite its importance, there is limited empirical evidence guiding what constitutes an effective or preferred viewpoint from the user's perspective. In this paper, we present a systematic investigation into user-preferred viewpoints in 3D graph visualisations. We conducted a controlled study with 23 participants in a virtual reality environment, where users selected their most and least preferred viewpoints for 36 different graphs varying in size and layout. From this data, enriched by qualitative feedback, we distil common strategies underlying viewpoint choice. We further analyse the alignment of user preferences with classical 2D aesthetic criteria (e.g., Crossings), 3D-specific measures (e.g., Node-Node Occlusion), and introduce a novel measure capturing the perceivability of a graph's principal axes (Isometric Viewpoint Deviation). Our data-driven analysis indicates that Stress, Crossings, Gabriel Ratio, Edge-Node Overlap, and Isometric Viewpoint Deviation are key indicators of viewpoint preference. Beyond our findings, we contribute a publicly available dataset consisting of the graphs and computed aesthetic measures, supporting further research and the development of viewpoint evaluation measures for 3D graph drawing.