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Surrounded by Friends: Design and Evaluation of Immersive Layouts of Egocentric Network for Visual Analytics
Kentaro Takahira, Takanori Fujiwara, Wong Kam-Kwai, Kento Shigyo, Leni Yang, Hiroaki Natsukawa, Yalong Yang, Huamin Qu
TL;DR
Egocentric network visualization must balance ego–alter strength encoding with alter-topology comprehension, while desktop displays and existing immersive approaches leave important design and evaluation gaps. The paper identifies design dimensions, proposes four VR layouts, and evaluates them with 24 participants. Cube was particularly effective for connection-strength tasks, whereas Spherical was more effective for conveying alter topology.
Problem
Desktop egocentric visualization becomes cluttered as networks grow, while immersive strategies for encoding ego–alter strength and supporting detailed task analysis remain underexplored.
Method
The authors identify desirable properties and four design dimensions, propose Cube, Cylindrical, Radial, and Spherical layouts, and evaluate them in a 24-participant user study.
Results
Cube was particularly effective for assessing ego–alter connection strength, whereas Spherical was more effective for conveying alter topology.
Takeaways & Limitations
No single layout was universally optimal; layout choice should align viewpoint and spatial design with the egocentric analysis task.
Takeaways & Limitations
The ego-networks were modest, narrow in density, and static, so findings require testing on larger, denser, and dynamic networks.
Abstract
from arXiv · showhide
This paper explores design considerations for egocentric network layouts in immersive environments, providing fresh empirical insights that enhance egocentric network analysis. An egocentric network focuses on the topological and semantic relationships around a focal node (ego) and its neighboring nodes (alters), targeting local sub-networks rather than the whole network. Traditional desktop environments, limited by display constraints, often face visual clutter as node numbers grow. Building on recent findings that immersive environments enhance network analysis, we explore layouts tailored for these spaces. We begin by identifying essential design properties and dimensions for egocentric network layouts, taking into account the unique features of immersive environments. Based on these, we design four layouts-Cube, Cylindrical, Radial, and Spherical-that vary across design dimensions. We evaluate these layouts in a user study with 24 participants completing egocentric analysis tasks. Our study suggests that Cube performed well for tasks focused on ego-alter connection strength. In contrast, Spherical was more effective for understanding alter topology, minimizing occlusion, and efficiently utilizing 3D space. These findings inform design implications for future immersive egocentric network layouts.
1 Introduction
Egocentric visualization must jointly show ego–alter connection strength and alter topology, but desktop displays become cluttered as networks grow. This paper addresses the gap with four immersive layouts and a 24-participant evaluation.
- Egocentric analysis examines ego–alter connection strengths and topology among alters in local subnetworks.
- Desktop egocentric layouts struggle to balance connection-strength encoding with visual clarity as network size increases.Radial layouts are intuitive and space-efficient, but can become cluttered; alternative visual channels provide flexibility while weakening positional and hierarchy cues.
- VR provides stereoscopic depth, physical navigation, and direct manipulation, but effective layout strategies and task impacts remain underexplored.
- The authors identify four layout design dimensions and propose Cube, Cylindrical, Radial, and Spherical immersive layouts.The dimensions concern positional strength encoding, viewpoint assumptions, spatial layout dimensions, and exploration strategies.
- A user study with 24 participants found Cube effective for ego–alter strength tasks and Spherical more effective for alter-topology comprehension.The study also examined interaction patterns, task efficiencies, and design implications.
2 Related work
Prior work establishes immersive networks as promising for spatial and embodied analysis, but largely emphasizes broad overviews. Egocentric VR visualization remains insufficiently tested for detailed subnetworks and explicit ego–alter strength encoding.
- Egocentric networks support analyses of social relationships, information flow, disease transmission, and organizational communication.
- Traditional radial node-link diagrams encode connection strength through concentric positions but become cluttered as networks grow.
- Immersive environments offer stereoscopic depth, 360-degree space, and embodied interaction for interpreting three-dimensional network structures.These capabilities can reduce overlap and edge crossings and support intuitive exploration.
- Most immersive network visualization research emphasizes broad overviews rather than detailed subnetwork analysis.
- Existing egocentric VR work facilitates local topology comprehension but lacks explicit ego–alter relationship-strength encoding and task-specific evaluation.
3 Egocentric Network Layouts
The paper frames immersive egocentric layouts around connection-strength encoding, alter topology, viewpoint, dimensionality, and exploration mode. Four layouts instantiate different combinations of these choices and exploit depth and viewpoint-driven navigation.
- The layout goals are effective encoding of ego–alter connection strength and effective representation of alter–alter topology.
- Four design dimensions organize immersive layouts: positional strength encoding, viewpoint assumptions, spatial dimensions, and primary exploration modes.
- Fixed-viewpoint layouts can reduce occlusion and cognitive demands, whereas viewpoint-independent layouts support multiple perspectives but require more movement and adjustment.
- Immersive Layouts: The four layouts collectively span broad combinations of the design dimensions for comparative user-study analysis.The designs use immersive depth and flexible viewpoint-driven navigation beyond traditional 2D representations.
- Immersive Layouts: The layouts omit the ego node and its links to first-degree alters to reduce clutter, while visually distinguishing first- and second-degree alters.
- Cube Layout: Cube starts from a 2D force-directed layout and places nodes along depth according to ego–alter strength, with stronger ties closer to the initial viewpoint.Its exocentric view and unanchored depth layers require users to interpret depth without a shared reference plane.
- Cylindrical Layout: Cylindrical bends the Cube arrangement around the user and encodes strength by radial distance from the vertical axis.It reduces occlusion for a fixed viewpoint but limits side views and relies on frontal depth cues.
- Radial Layout: Radial places nodes on the floor in concentric circles, using distance from the center to represent ego–alter connection strength.Its fixed radial distances limit placement flexibility and can cause occlusion or link crossings.
4 User Study
The user study examined performance across immersive egocentric network layouts after institutional review approval and informed consent.
- The study evaluated user performance across different immersive egocentric network layouts with institutional review approval and signed participant consent.
4.1 Tasks
The study used seven tasks covering ego–alter connection strength, alter topology, and their combination. Tasks required selecting, comparing, counting, or integrating spatial and topological cues.
- Connection strength: Tasks 1–2 assessed ego–alter connection strength by identifying alters with the highest or lowest connection strength.The lowest-strength task used three highlighted alters and required assessing distant nodes.
- Alter topology: Task 3 identified the highlighted alter with the most neighboring nodes, including both 1st and 2nd alters.This task targeted centrality within the alter network.
- Alter topology: Task 4 required finding a common neighbor between two highlighted nodes, with each pair sharing three to six common neighbors.Participants could observe multiple nodes simultaneously or recall their connections.
- Alter topology: Task 5 counted the 2nd alters connected to a highlighted 1st alter, with one to six 2nd alters available.Participants submitted answers using an interactive panel.
- Alter topology: Task 6 counted clusters of mutually connected 1st alters, ranging from two to four clusters.It required the broadest view among topology tasks because participants observed the largest number of nodes simultaneously.
- Integrated analysis: Task 7 identified the common neighbor with the strongest ego connection, combining strength assessment with topology understanding.The task examined how participants integrated positional and topological cues.
4.2 Evaluation Metrics
Performance was evaluated through completion time, accuracy, and movement distance, supplemented by questionnaire and interview feedback.
- Performance measures: The evaluation measured task completion time, accuracy rate, and movement distance in the immersive space.Time covered task start to response, while movement distance aggregated total camera travel.
- Subjective feedback: Qualitative feedback came from a questionnaire and a semi-structured interview.
4.3 Experiment Design
The experiment compared four immersive layouts in a within-subjects study using controlled synthetic egocentric networks and standardized VR procedures.
- Study design: The within-subjects study compared Cube, Cylindrical, Radial, and Spherical layouts across 28 tasks per participant.Each participant completed four layouts multiplied by seven tasks.
- Study design: Each layout used a unique network dataset per participant, while the same dataset was maintained across tasks within each layout.This reduced learning effects and focused comparisons on layouts rather than tasks.
- Datasets: Synthetic datasets were derived from a widely studied co-authorship network and modeled larger ego-network properties.Reference ranges included 10–30% edge density, 20–40 first-degree alters, 30–60 second-degree alters, and 2–4 clusters.
- VR environment: The VR environment used A-Frame, Three.js, and d3.js in a browser and ran at 80 FPS on Meta Quest 2 headsets.Participants navigated through physical movement and virtual controls.
- Navigation: Movement distance combined physical walking, controller navigation, and camera repositioning after resets.It was interpreted as overall navigation activity rather than physical effort.
- Participants: The study recruited 24 participants with varied VR and 3D-game experience.Most participants had limited network-visualization experience.
- Procedure: Participants received printed concept and task instructions, practiced in VR, and then completed the main tasks.Layouts and encoding methods were introduced with 2D diagrams and VR screenshots.
4.4 Guiding Questions
The guiding questions examined layout support for connection strength, alter topology, integrated analysis, user preference, and navigation strategies.
- Connection strength: GQ1 asked which layouts support accurate and efficient ego–alter connection-strength assessment and which hinder it.The expectations contrasted Radial’s anchored depth encoding with alternative layout properties.
- Alter topology: GQ2 asked which layouts facilitate accurate and efficient alter-topology identification, emphasizing minimal node and edge occlusion.Spherical was expected to perform well, whereas Radial’s planar constraints were expected to hinder topology tasks.
- Integrated analysis: GQ3 examined which layout best supports simultaneous understanding of connection strength and topology.The question sought the best balance when tasks require both properties.
- User preference: GQ4 asked which layouts users prefer and expect least, including whether Cylindrical would be most preferred.The expectation linked viewpoint-optimized exocentric layouts with reduced motion sickness.
- User strategies: GQ5 examined how strategies differ across layouts, including reliance on head motion versus movement and viewpoint adjustments.Cylindrical and Spherical were expected to support minimal movement, while Cube and Radial were expected to require more movement to resolve occlusions and positional cues.
5 Result
The study compared four immersive layouts across accuracy, completion time, movement, and subjective feedback. Cube supported ego–alter strength tasks, while Spherical and Cylindrical often supported topology comprehension and reduced occlusion.
- Task 2: Ego-Alter Strength: Accuracy was 96% for Cube and 100% for Radial versus 71% for both Cylindrical and Spherical in Task 2.The overall accuracy difference was significant (p = .003), although no pairwise comparison survived Bonferroni correction.
- Task 5: Local Topology: Spherical achieved the highest average accuracy in Task 5, with Cylindrical close behind, although layout differences were not statistically significant.Both layouts offered low occlusion from the initial viewpoint, whereas Radial had the lowest accuracy and significantly longer completion times.
- Task 6: Alter Topology: Cube and Spherical yielded significantly higher accuracy than Radial in Task 6, with p < .0083 for both pairwise comparisons.Cube and Spherical also had the highest movement distances, at 1062m and 900m respectively, as participants used different strategies to understand overall topology.
- Task 7: Integrated Analysis: Cube maintained relatively high accuracy in Task 7, but its sequential front- and side-view strategy produced the largest average movement.Spherical and Cylindrical showed more pronounced accuracy drops on this integrated strength-and-topology task.
- Subjective Feedback: Cylindrical and Cube received the highest positive ratings for ease of understanding strength encoding, at 92% and 75%, respectively.Participants generally found depth-based strength encoding intuitive across layouts.
- Subjective Feedback: Radial and Spherical were perceived as most physically demanding, despite Cube producing the highest movement distances.Stationary rotation in Radial and Spherical was associated with greater fatigue and motion sickness, whereas movement in Cube felt more natural.
- Subjective Feedback: Cylindrical received the highest overall preference at 42%, followed by Cube at 29%.Participants associated Cylindrical with minimal occlusion, broad initial visibility, and less body rotation, while Radial was least preferred.
6 Discussion
The discussion distills study findings into design guidance for immersive egocentric layouts. It emphasizes balancing strength encoding with topology clarity, matching viewpoints to tasks, and recognizing the bounded scope of the recommendations.
- Scope of Guidelines: The guidelines are bounded by the study conditions and should be treated as informed suggestions.The authors frame the recommendations as derived from the observed effects of layouts on user performance and experience.
- Strength Encoding: Anchored depth encoding is most effective when nodes share a single reference plane, supporting accurate ego–alter strength judgments.The Radial layout demonstrates this benefit even for distant nodes.
- Strength Encoding: Multi-angle viewing helps Cube users verify spatial relationships when nodes are distributed across multiple planes.Rotation or repositioning widgets can further support non-anchored depth encoding.
- Topology Tasks: Spherical is well suited to localized topology tasks because flexible 3D distribution, reduced occlusion, and an optimized initial viewpoint support local exploration.Its egocentric perspective can hinder comprehensive views and cluster recall; orientation aids may mitigate this limitation.
- Topology Tasks: Radial is generally inadequate for topology tasks because restrictive placement, node occlusion, and edge overlap hinder structural interpretation.The discussion recommends more flexible 3D layouts for topology-focused analysis.
- Combined Tasks: Cube balances ego–alter strength and topology representation, producing higher accuracy in complex combined tasks at the cost of considerable movement.Easy viewpoint manipulation or layout transitions could reduce user effort.
- Design Dimensions: Effective strength encoding and expressive topology representation involve a trade-off that multi-angle layouts such as Cube help users navigate.Anchored depth improves clarity but limits spatial flexibility, whereas non-anchored depth can impair distance perception.
- Design Dimensions: Fixed viewpoints in Cylindrical and Spherical reduce occlusion and movement for topology tasks but limit multi-angle exploration.The choice depends on whether rapid structural grasp or perspective-based confirmation is more important.
7 Limitations
The study’s limitations concern the modest, static network data, non-expert participants, and simultaneous variation across multiple layout dimensions.
- Network Diversity and Dynamics: The ego-networks were modest in size, narrow in density, and static, limiting verified applicability to larger, denser, or dynamic settings.The authors suggest Spherical may accommodate larger or denser graphs, but this remains unverified.
- Participant Expertise: Participants were not domain experts, so professional analysts may prioritize different cues, navigate differently, and prefer different layouts.Applicability to professional contexts remains to be verified with experts.
- Confounding of Design Dimensions: Because layouts varied across multiple design dimensions simultaneously, performance differences cannot be attributed to individual design choices.The comparison evaluates complete layouts as coherent designs used in practice.
8 Conclusion
The study identifies design properties and dimensions for immersive egocentric network visualization, proposes four layouts, and evaluates them with 24 participants. No layout was universally optimal: Cube favored ego–alter strength assessment, while Spherical better conveyed alter topology.
- 8 Conclusion: The study identified desirable properties and design dimensions for visualizing egocentric networks in immersive environments.These insights guided the proposed layout designs.
- 8 Conclusion: The authors proposed four immersive layouts—Cube, Cylindrical, Radial, and Spherical—and evaluated them with 24 participants performing representative analysis tasks.The layouts represent different design choices across the identified dimensions.
- 8 Conclusion: No single layout was universally optimal: Cube was particularly effective for ego–alter connection strength, whereas Spherical better conveyed alter topology.Layout design also shaped users’ movement, task efficiency, and subjective experience.
- 8 Conclusion: The findings support aligning viewpoint optimization with task demands and balancing strength-encoding clarity against topological expressiveness.These principles were derived as design guidelines for immersive egocentric network layouts.