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

2025-06-10

AI Summary

This study addresses the lack of empirical evidence regarding user viewpoint preferences in 3D graph visualization. We conducted a controlled VR experiment with 23 participants to systematically analyze their optimal/worst viewpoint selections and qualitative feedback across 36 graph structures and 36 viewpoints. We introduce the “isometric viewpoint bias” — the first quantitative metric characterizing systematic deviation from isometric projections. Five strong predictive features—Stress, Crossings, Gabriel Ratio, among others—were identified, demonstrating that user preferences significantly diverge from classical 2D graph aesthetics. We release the first publicly available 3D graph viewpoint dataset, featuring multi-dimensional aesthetic metrics and expert-annotated optimal viewpoints. Additionally, we provide a reproducible evaluation framework and an open-source toolchain. Collectively, these contributions establish an empirical foundation for intelligent, automatic optimization of 3D graph viewpoints.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Identify user-preferred viewpoints in 3D graph visualizations
• Evaluate alignment of user preferences with aesthetic criteria
• Develop measures for viewpoint evaluation in 3D graphs

Innovation

• Systematic study of user-preferred 3D graph viewpoints
• Novel measure for graph principal axes perceivability
• Data-driven analysis of key viewpoint preference indicators