EAGER: Link Free Graph Visualization for Exploring Large Complex Graphs

EAGER：用于探索大型复杂图的链接自由图可视化

• 批准号: 0946400
• 负责人: Jing Yang
• 金额: $ 14.44万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0946400&HistoricalAwards=false
• 关键词: EAGER; Link; Free; Graph; Visualization

This project develops proof-of-concept examples for a novel graph visualization technique named Link Free Graph Visualization (LFGV). The research addresses problems in existing graph visualization techniques: (1) becoming cluttered when visualizing large graphs; (2) having limited applicability to complex graphs, such as graphs with high dimensional node attributes and time evolving graphs; (3) misleading users due to the information loss caused by 2D or 3D node layouts and efforts toward increasing scalability. The research leads to new graph visualization techniques with better scalability, better applicability to complex graphs, and less misleading insights than NLDs. LFGV first projects a graph to a multidimensional space while preserving major graph topology information. LFGV then uses extended multidimensional visualization techniques to visualize the projection for exploring graph topology and other information carried by the graph, such as multidimensional node attributes. This project develops a working LFGV prototype for plain/multivariate graphs and conducts evaluations to prove that LFGV is viable. It also develops a weblog visual analysis approach to provide a test bed and an application example for LFGV. Graph visualization is widely used in lots of applications, such as social network analysis, bioinformatics, and web information exploration. LFGV impacts the application fields where large complex graphs need to be analyzed. The weblog visual analysis approach has a direct impact on online information browsing, retrieval, and analysis. The project publishes software for research and educational purposes.

该项目为一种名为“无链接图形可视化”(LFGV) 的新型图形可视化技术开发概念验证示例。该研究解决了现有图形可视化技术中的问题：（1）可视化大型图形时变得混乱； (2)对复杂图的适用性有限，例如具有高维节点属性的图和时间演化图； (3) 由于 2D 或 3D 节点布局和提高可扩展性的努力造成的信息丢失而误导用户。该研究带来了新的图形可视化技术，与 NLD 相比，该技术具有更好的可扩展性、对复杂图形的更好适用性以及更少的误导性见解。 LFGV 首先将图投影到多维空间，同时保留主要图拓扑信息。然后，LFGV 使用扩展的多维可视化技术来可视化投影，以探索图拓扑和图携带的其他信息，例如多维节点属性。该项目为简单/多元图开发了一个工作 LFGV 原型，并进行评估以证明 LFGV 是可行的。它还开发了一种博客可视化分析方法，为 LFGV 提供测试平台和应用示例。图可视化广泛应用于许多应用中，例如社交网络分析、生物信息学和网络信息探索。 LFGV 影响需要分析大型复杂图的应用领域。博客可视化分析方法直接影响在线信息的浏览、检索和分析。该项目发布用于研究和教育目的的软件。