被好友环绕：面向视觉分析的自我中心网络沉浸式布局设计与评估
Surrounded by Friends: Design and Evaluation of Immersive Layouts of Egocentric Network for Visual Analytics
浏览论文内容
中文总结 AI 辅助
该研究针对沉浸式环境设计了四种自我中心网络布局，经24人用户研究发现Cube适用于连接强度分析、Spherical适用于拓扑理解等任务，为相关布局设计提供了实证依据。
中文摘要 AI 辅助
本文探讨了沉浸式环境下自我中心网络布局的设计考量，提供了可改进自我中心网络分析的全新实证见解。自我中心网络聚焦于中心节点（ego）及其邻接节点（alters）的拓扑与语义关系，针对的是局部子网络而非整个网络。传统桌面环境受限于显示空间，随着节点数量增加常出现视觉混乱。基于沉浸式环境可提升网络分析的最新研究发现，本文探索适配这类空间的布局。首先，结合沉浸式环境的独特特征，确定自我中心网络布局的关键设计属性与维度；在此基础上，设计了Cube、Cylindrical、Radial、Spherical四种在设计维度上存在差异的布局。通过24名参与者完成自我中心分析任务的用户研究对这些布局进行评估，研究表明：Cube在聚焦ego-alters连接强度的任务中表现良好；相比之下，Spherical在理解alters拓扑、最小化遮挡以及高效利用3D空间方面更为有效。这些发现为未来沉浸式自我中心网络布局的设计提供了启示。
英文摘要
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.
