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: 1835893
• 负责人: Luke Harmon
• 金额: $ 12.25万
• 依托单位: Regents of the University of Idaho
• 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=1835893&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.

多变量网络--将与多个不同变量相关联的实体连接在一起的数据集--是一系列高影响力问题的关键数据表示，从理解我们的身体如何工作到揭示社交媒体如何影响社会。这些数据表示形式丰富而复杂地反映了世界上存在的多方面关系。通过使用多元网络对问题进行推理，分析师可以提出仅限于显式连接的问题：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？什么样的细胞类型支持不同类型的大脑功能？这类问题需要了解与实体的属性和连接性相关的模式和趋势，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的日益重要的驱动力，网络的数据集也变得越来越复杂。这些网络捕获关于实体之间的关系以及实体和连接的属性的信息。目前在实践中使用的工具对有关网络的推理提供的支持非常有限，而且用户与其交互的方式也很有限。由于缺乏支持，分析人员和科学家只能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。这个项目旨在通过开发一个健壮、灵活、安全和可持续的开源可视化分析系统Multinet来填补现有网络基础设施生态系统中关于多元网络推理的这一关键空白。Multinet的目标是改变用于推理和分析多变量网络的可视化分析能力的格局。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)重塑底层网络结构以使网络形成非常适合解决分析问题的形状；以及(3)利用来源数据支持计算工作流中的重复性、通信和集成。这些能力将允许科学家提出关于网络数据集的新问题，并导致对广泛紧迫主题的洞察。为了实现这一目标，我们将在四个与生物学、神经科学、社会学和地质学领域的科学家深度合作的案例研究中对Multinet的设计进行基础研究。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。