TRIPODS+X:RES:Collaborative Research: Multi-Level Graph Representation for Exploring Big Data

TRIPODS X:RES:协作研究：探索大数据的多级图表示

• 批准号: 1839167
• 负责人: Katy Borner
• 金额: $ 30万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839167&HistoricalAwards=false
• 关键词: TRIPODS+X; RES; Collaborative; Research; Multi

Working with social, topical, financial, transportation, biological, and other networks requires a better understanding of their structure and properties. Standard network visualizations of such large real-world networks often resemble hairballs that provide little actionable insight. This project aims to design, implement, and deploy efficient algorithms for multi-level network representations that support interactive exploration by general audiences. Using the familiar Google map metaphor, these algorithms will make it easy to identify important nodes, major pathways, and clusters across multiple levels. Unlike existing methods for visualizing multi-level networks based on meta-nodes and meta-edges, the new visualizations will provide real nodes (prototypes) and real paths (backbones) for each level, similar to geographic maps that show real cities and real roads at every level of detail.The proposed work contributes to graph algorithms by designing and implementing novel and efficient algorithms for interactive analysis and visualization of large, multi-level networks, information visualization with new methods for multi-level network visualization based on the familiar map metaphor, and science mapping standards by providing effective means to explore and use large-scale, multi-level science maps of our collective scholarly knowledge as well as workforce needs. The first goal is to design efficient algorithms for computing Multi-Level Graph Spanners (MLGS) in support of visual analytics tasks for large network exploration, navigation, and communication. The second goal is to utilize the MLGS representation in the context of network analysis and visualization by building a novel online visualization service for interacting with large networks, which combines the MLGS approach with clustering, layout and map-like visualization. The third goal is to develop a new approach for science and workforce classification, lookup, and topical mapping service by applying the MLGS approach to the Web of Science publication data (64 million publications and 1 billion citations) to compute a multi-level map of scientific development. The fourth goal is to validate the new algorithms and visualizations by evaluating both the algorithms and the system using quantitative and qualitative metrics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

使用社交、话题、金融、交通、生物和其他网络需要更好地了解它们的结构和属性。此类大型现实世界网络的标准网络可视化通常类似于毛团，几乎无法提供可操作的见解。该项目旨在设计、实现和部署用于多级网络表示的高效算法，以支持普通受众的交互式探索。使用熟悉的谷歌地图比喻，这些算法将可以轻松识别多个级别的重要节点、主要路径和集群。与现有基于元节点和元边的多层网络可视化方法不同，新的可视化将为每个级别提供真实的节点（原型）和真实的路径（主干），类似于显示真实城市和真实道路的地理地图所提出的工作通过设计和实现新颖且高效的算法来对大型多级网络的交互式分析和可视化、信息可视化以及基于熟悉的地图的多级网络可视化新方法的信息可视化做出贡献。隐喻和科学制图标准通过提供有效的手段来探索和使用我们集体学术知识和劳动力需求的大规模、多层次的科学地图。第一个目标是设计用于计算多级图扳手 (MLGS) 的高效算法，以支持大型网络探索、导航和通信的可视化分析任务。第二个目标是通过构建一种用于与大型网络交互的新型在线可视化服务，将 MLGS 方法与聚类、布局和类地图可视化相结合，在网络分析和可视化的背景下利用 MLGS 表示。第三个目标是通过将 MLGS 方法应用于 Web of Science 出版物数据（6400 万篇出版物和 10 亿次引用）来计算科学和劳动力分类、查找和主题制图服务的多级地图，从而开发一种新的科学和劳动力分类、查找和主题制图服务方法。科学发展。第四个目标是通过使用定量和定性指标评估算法和系统来验证新算法和可视化。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)

绘制 20 年来人工智能、机器人和物联网的共同演化图（1998-2017）

• DOI: 10.1371/journal.pone.0242984
• 发表时间: 2020-12
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Börner, Katy;Scrivner, Olga;Cross, Leonard E.;Gallant, Michael;Ma, Shutian;Martin, Adam S.;Record, Lisel;Yang, Haici;Dilger, Jonathan M.
• 通讯作者: Dilger, Jonathan M.

Forecasting innovations in science, technology, and education

预测科学、技术和教育领域的创新

• DOI: 10.1073/pnas.1818750115
• 发表时间: 2018-12-10
• 期刊: Proceedings of the National Academy of Sciences
• 作者: K. Börner;William B. Rouse;P. Trunfio;H. Stanley
• 通讯作者: H. Stanley

The impact of air transport availability on research collaboration: A case study of four universities

航空运输可用性对研究合作的影响：四所大学的案例研究

• DOI: 10.1371/journal.pone.0238360
• 发表时间: 2020-09
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Ploszaj, Adam;Yan, Xiaoran;Börner, Katy
• 通讯作者: Börner, Katy

Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy

研究、教育和工作之间的技能差异揭示了为数据经济提供软技能的迫切需要

• DOI: 10.1073/pnas.1804247115
• 发表时间: 2018-12
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Börner, Katy;Scrivner, Olga;Gallant, Mike;Ma, Shutian;Liu, Xiaozhong;Chewning, Keith;Wu, Lingfei;Evans, James A.
• 通讯作者: Evans, James A.

A Scalable Method for Readable Tree Layouts

可读树布局的可扩展方法

• DOI: 10.1109/tvcg.2023.3274572
• 发表时间: 2023-05-09
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Kathryn E. Gray;Mingwei Li;R. Ahmed;Md. Khaledur Rahman;A. Azad;S. Kobourov;K. Börner
• 通讯作者: K. Börner