Collaborative Research: Framework: Software: HDR: Reproducible Visual Analysis of Multivariate Networks with MultiNet

合作研究：框架：软件：HDR：使用 MultiNet 对多元网络进行可重复的视觉分析

• 批准号: 1835904
• 负责人: Alexander Lex
• 金额: $ 189.97万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835904&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Software; HDR

Multivariate networks -- datasets that link together entities that are associated with multiple different variables -- are a critical data representation for a range of high-impact problems, from understanding how our bodies work to uncovering how social media influences society. These data representations are a rich and complex reflection of the multifaceted relationships that exist in the world. Reasoning about a problem using a multivariate network allows an analyst to ask questions beyond those about explicit connectivity alone: Do groups of social-media influencers have similar backgrounds or experiences? Do species that co-evolve live in similar climates? What patterns of cell-types support different types of brain functions? Questions like these require understanding patterns and trends about entities with respect to both their attributes and their connectivity, leading to inferences about relationships beyond the initial network structure. As data continues to become an increasingly important driver of scientific discovery, datasets of networks have also become increasingly complex. These networks capture information about relationships between entities as well as attributes of the entities and the connections. Tools used in practice today provide very limited support for reasoning about networks and are also limited in the how users can interact with them. This lack of support leaves analysts and scientists to piece together workflows using separate tools, and significant amounts of programming, especially in the data preparation step. This project aims fill this critical gap in the existing cyber-infrastructure ecosystem for reasoning about multivariate networks by developing MultiNet, a robust, flexible, secure, and sustainable open-source visual analysis system. MultiNet aims to change the landscape of visual analysis capabilities for reasoning about and analyzing multivariate networks. The web-based tool, along with an underlying plug-in-based framework, will support three core capabilities: (1) interactive, task-driven visualization of both the connectivity and attributes of networks, (2) reshaping the underlying network structure to bring the network into a shape that is well suited to address analysis questions, and (3) leveraging provenance data to support reproducibility, communication, and integration in computational workflows. These capabilities will allow scientists to ask new classes of questions about network datasets, and lead to insights about a wide range of pressing topics. To meet this goal, we will ground the design of MultiNet in four deeply collaborative case studies with domain scientists in biology, neuroscience, sociology, and geology.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.

多元网络（将与多个不同变量相关的实体链接在一起的数据集）是一系列高影响问题的关键数据表示，从了解我们的身体如何工作到揭示社交媒体如何影响社会。这些数据表示是世界上存在的多方面关系的丰富而复杂的反映。使用多元网络推理问题使分析师能够提出除显式连接之外的问题：社交媒体影响者群体是否具有相似的背景或经历？共同进化的物种生活在相似的气候中吗？哪些细胞类型模式支持不同类型的大脑功能？此类问题需要了解实体在属性和连接性方面的模式和趋势，从而推断出初始网络结构之外的关系。随着数据继续成为科学发现日益重要的驱动力，网络数据集也变得越来越复杂。这些网络捕获有关实体之间的关系以及实体和连接的属性的信息。当今实践中使用的工具为网络推理提供的支持非常有限，并且用户与网络交互的方式也受到限制。由于缺乏支持，分析师和科学家不得不使用单独的工具和大量的编程来拼凑工作流程，尤其是在数据准备步骤中。该项目旨在通过开发 MultiNet（一个强大、灵活、安全且可持续的开源视觉分析系统）来填补现有网络基础设施生态系统中用于推理多元网络的关键空白。 MultiNet 旨在改变用于推理和分析多元网络的视觉分析能力的格局。基于网络的工具以及基于插件的底层框架将支持三个核心功能：（1）网络连接和属性的交互式、任务驱动的可视化，（2）重塑底层网络结构使网络形成非常适合解决分析问题的形状，以及（3）利用来源数据来支持计算工作流程中的再现性、通信和集成。这些功能将使科学家能够提出有关网络数据集的新问题，并得出有关广泛紧迫主题的见解。为了实现这一目标，我们将在与生物学、神经科学、社会学和地质学领域科学家的四个深度合作案例研究中设计 MultiNet。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

Origraph: Interactive Network Wrangling

Origraph：互动网络争论

• DOI: 10.1109/vast47406.2019.8986909
• 发表时间: 2019-10
• 期刊: IEEE Conference on Visual Analytics Science and Technology (VAST
• 作者: Bigelow, Ale;Nobre, Carolina;Meyer, Miriah;Lex, Alexander
• 通讯作者: Lex, Alexander

Data Hunches: Incorporating Personal Knowledge into Visualizations

数据预感：将个人知识融入可视化中

• DOI: 10.1109/tvcg.2022.3209451
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Lin, Haihan;Akbaba, Derya;Meyer, Miriah;Lex, Alexander
• 通讯作者: Lex, Alexander

The State of the Art in Visualizing Multivariate Networks

多元网络可视化的最新技术

• DOI: 10.1111/cgf.13728
• 发表时间: 2019-05-30
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: C. Nobre;Miriah D. Meyer;M. Streit;A. Lex
• 通讯作者: A. Lex

Troubling Collaboration: Matters of Care for Visualization Design Study

令人烦恼的协作：可视化设计研究的注意事项

• DOI: 10.1145/3544548.3581168
• 发表时间: 2023-04
• 期刊: SIGCHI Conference on Human Factors in Computing Systems (CHI
• 作者: Akbaba, Derya;Lange, Devin;Correll, Michael;Lex, Alexander;Meyer, Miriah
• 通讯作者: Meyer, Miriah

reVISit: Looking Under the Hood of Interactive Visualization Studies

reVISit：深入探究交互式可视化研究

• DOI: 10.1145/3411764.3445382
• 发表时间: 2021-05
• 期刊: SIGCHI Conference on Human Factors in Computing Systems (CHI
• 作者: Nobre, Carolina;Wootton, Dylan;Cutler, Zach;Harrison, Lane;Pfister, Hanspeter;Lex, Alexander
• 通讯作者: Lex, Alexander