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.

与社会、话题、金融、交通、生物和其他网络合作需要更好地了解它们的结构和属性。对这样的大型现实世界网络的标准网络可视化往往像是毛球，提供的可操作洞察力很小。该项目旨在设计、实现和部署支持普通受众交互探索的多层次网络表示的高效算法。使用熟悉的谷歌地图比喻，这些算法将使识别多个级别的重要节点、主要路径和集群变得容易。与现有的基于元节点和元边的多级网络可视化方法不同，新的可视化将为每一层提供真实的节点(原型)和真实的路径(主干)，类似于在每一层细节上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用于大型、多级网络的交互分析和可视化的新颖而高效的算法来促进图形算法，通过基于熟悉的地图隐喻的多级网络可视化的新方法来实现信息可视化，以及通过提供有效的手段来探索和使用大比例尺的科学制图标准，我们的集体学术知识和劳动力需求的多层次科学地图。第一个目标是设计高效的算法来计算多层图Spanner(MLGS)，以支持大型网络探索、导航和通信的视觉分析任务。第二个目标是在网络分析和可视化的背景下利用MLGS表示法，建立一种新的用于与大型网络互动的在线可视化服务，该服务将MLGS方法与集群、布局和类似地图的可视化相结合。第三个目标是开发一种新的科学和劳动力分类、查找和专题地图服务方法，将MLGS方法应用于科学网出版物数据(，百万篇出版物和10亿篇引文)，计算多层次的科学发展地图。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的算法和可视化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

A Scalable Method for Readable Tree Layouts

• DOI: 10.1109/tvcg.2023.3274572
• 发表时间: 2023-05
• 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

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

• DOI: 10.1073/pnas.1804247115
• 发表时间: 2018-12-11
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Boerner, Katy;Scrivner, Olga;Evans, James A.
• 通讯作者: Evans, James A.

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

• DOI: 10.1371/journal.pone.0238360
• 发表时间: 2018-11
• 期刊: PLoS ONE
• 影响因子: 3.7
• 作者: Adam Ploszaj;Xiaoran Yan;K. Börner

Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments

• DOI: 10.1073/pnas.1807180116
• 发表时间: 2019-02-05
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Boerner, Katy;Bueckle, Andreas;Ginda, Michael
• 通讯作者: Ginda, Michael