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

多元网络——将与多个不同变量相关的实体联系在一起的数据集——是一系列高影响力问题的关键数据表示，从了解我们的身体如何运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。使用多元网络对问题进行推理，使分析师能够提出一些问题，而不仅仅是关于明确的联系：社交媒体影响者群体是否具有相似的背景或经历？共同进化的物种是否生活在相似的气候中？什么类型的细胞支持不同类型的大脑功能？像这样的问题需要理解实体的属性和连接性方面的模式和趋势，从而推断出超出初始网络结构的关系。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关系的信息，以及实体和连接的属性。目前在实践中使用的工具对网络推理提供的支持非常有限，而且在用户如何与网络交互方面也受到限制。由于缺乏支持，分析师和科学家不得不使用不同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多网络来填补现有网络基础设施生态系统中对多元网络进行推理的关键空白，多网络是一个强大、灵活、安全和可持续的开源可视化分析系统。多网络旨在改变对多元网络进行推理和分析的可视化分析能力。基于web的工具，以及基于底层插件的框架，将支持三个核心功能：(1)网络连接性和属性的交互式、任务驱动的可视化；(2)重塑底层网络结构，使网络形成一个非常适合解决分析问题的形状；(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，并导致对广泛的紧迫主题的见解。为了实现这一目标，我们将与生物学、神经科学、社会学和地质学领域的科学家在四个深度合作的案例研究中奠定多网设计的基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.