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

TRIPODS X:RES:CollaborativeResearch：用于探索大数据的多级图表示

基本信息

批准号：
1839274
负责人：
Stephen Kobourov
金额：
$ 30万
依托单位：
University of Arizona
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 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839274&HistoricalAwards=false
关键词：
TRIPODS+X RES CollaborativeResearch Multi Level

项目摘要

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，局部，财务，运输，生物学和其他网络合作需要更好地了解其结构和特性。如此大的现实世界网络的标准网络可视化通常类似于几乎没有可行的见解的毛球。该项目旨在设计，实施和部署有效的算法，以用于支持一般受众的交互式探索的多级网络表示。使用熟悉的Google Map隐喻，这些算法将使跨多个级别识别重要节点，主要途径和群集变得容易。与现有用于基于元节点和元边缘的多级网络可视化的方法不同，新的可视化将为每个级别提供真实的节点（原型）和真实路径（原型）和真实路径（原型）和真实的路径（骨干），类似于地理图，类似于在每个级别上显示的真实城市和真实的道路，这些详细信息和详细范围内的分析和实现了较大的小说和效率，并有效地构成了各种各样的小说，并有效地构成了各种各样的小说，并有效地构成了各种各样的小说和效率，并有效地构成了各种各样的小说，并有效地构成了各种各样的小说，并有效地进行了各种效率，并有效地构成了各种各样的小说，并有效地构成了各种各样的小说，并有效地构成了各种各样的小说和效率多层网络，使用新方法的信息可视化，用于基于熟悉的地图隐喻的多级网络可视化以及科学映射标准，通过提供有效的手段来探索和使用我们集体学术知识以及劳动力需求的大规模，多层科学地图。第一个目标是设计有效的算法，用于计算多级图形跨度（MLG），以支持大型网络探索，导航和通信的视觉分析任务。第二个目标是通过构建一种新颖的在线可视化服务来利用MLGS表示，以与大型网络进行交互，该网络将MLGS方法与聚类，布局和地图般的可视化结合在一起。第三个目标是通过将MLGS方法应用于科学出版物出版物数据（6400万张出版物和10亿引用）来计算科学发展的多层次地图，以开发一种新的科学和劳动力分类，查找和局部映射服务。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的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来获得支持的。

项目成果

期刊论文数量（31）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Approximation Algorithms for Priority Steiner Tree Problems

优先斯坦纳树问题的近似算法

DOI：
发表时间：
2021
期刊：
27th International Computing and Combinatorics Conference (COCOON
影响因子：
0
作者：
Spence, R.;Kobourov, S.;Sahneh, F.
通讯作者：
Sahneh, F.

Graph spanners: A tutorial review

图形扳手：教程回顾

DOI：
10.1016/j.cosrev.2020.100253
发表时间：
2020
期刊：
Computer Science Review
影响因子：
12.9
作者：
Ahmed, Reyan;Bodwin, Greg;Sahneh, Faryad Darabi;Hamm, Keaton;Latifi Jebelli, Mohammad Javad;Kobourov, Stephen;Spence, Richard
通讯作者：
Spence, Richard

The Turing Test for Graph Drawing Algorithms

绘图算法的图灵测试