Egocentric networks, often visualized as node-link diagrams, portray the complex relationship (link) dynamics between an entity (node) and others. However, common analytics tasks are multifaceted, encompassing interactions among four key aspects: strength, function, structure, and content. Current node-link visualization designs may fall short, focusing narrowly on certain aspects and neglecting the holistic, dynamic nature of egocentric networks. To bridge this gap, we introduce SpreadLine, a novel visualization framework designed to enable the visual exploration of egocentric networks from these four aspects at the microscopic level. Leveraging the intuitive appeal of storyline visualizations, SpreadLine adopts a storyline-based design to represent entities and their evolving relationships. We further encode essential topological information in the layout and condense the contextual information in a metro map metaphor, allowing for a more engaging and effective way to explore temporal and attribute-based information. To guide our work, with a thorough review of pertinent literature, we have distilled a task taxonomy that addresses the analytical needs specific to egocentric network exploration. Acknowledging the diverse analytical requirements of users, SpreadLine offers customizable encodings to enable users to tailor the framework for their tasks. We demonstrate the efficacy and general applicability of SpreadLine through three diverse real-world case studies (disease surveillance, social media trends, and academic career evolution) and a usability study.

翻译：自我中心网络通常以节点-链接图的形式进行可视化，用以描绘实体（节点）与其他实体之间复杂的关系（链接）动态。然而，常见的分析任务是多方面的，涵盖了强度、功能、结构和内容这四个关键方面的相互作用。当前的节点-链接可视化设计可能有所不足，它们往往狭隘地关注某些方面，而忽视了自我中心网络的整体性和动态性。为了弥合这一差距，我们引入了SpreadLine，这是一个新颖的可视化框架，旨在从微观层面支持对自我中心网络在这四个方面的可视化探索。利用故事线可视化直观易懂的优势，SpreadLine采用基于故事线的设计来表示实体及其不断演变的关系。我们进一步在布局中编码了关键的拓扑信息，并将上下文信息浓缩在地铁图的隐喻中，从而提供了一种更具吸引力且更有效的方式来探索基于时间和属性的信息。为了指导我们的工作，通过对相关文献的全面回顾，我们提炼出了一个任务分类法，以解决自我中心网络探索特有的分析需求。考虑到用户多样化的分析需求，SpreadLine提供了可定制的编码方式，使用户能够根据其任务定制该框架。我们通过三个不同的真实世界案例研究（疾病监测、社交媒体趋势和学术生涯演变）以及一项可用性研究，证明了SpreadLine的有效性和普遍适用性。