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工作到揭示社交媒体如何影响社会。这些数据表示丰富而复杂地反映了世界上存在的多方面关系。使用多变量网络对问题进行推理，可以让分析师提出超越那些仅关于显式连接的问题：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？像这样的问题需要理解实体的属性和连通性的模式和趋势，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越来越重要的驱动力，网络数据集也变得越来越复杂。这些网络捕获关于实体之间的关系以及实体和连接的属性的信息。今天在实践中使用的工具提供了非常有限的支持推理网络，也限制了用户如何与他们互动。这种支持的缺乏使得分析师和科学家使用单独的工具和大量的编程来拼凑工作流程，特别是在数据准备步骤中。该项目旨在填补现有网络基础设施生态系统中的这一关键空白，通过开发MultiNet来推理多元网络，MultiNet是一个强大，灵活，安全和可持续的开源视觉分析系统。 MultiNet旨在改变可视化分析功能的前景，以推理和分析多元网络。这个基于网络的工具，沿着一个基于插件的基础框架，将支持三个核心功能：（1）网络的连接性和属性的交互式、任务驱动的可视化，（2）重塑底层网络结构以使网络成为非常适合解决分析问题的形状，以及（3）利用起源数据来支持再现性、通信，和计算工作流的集成。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深入合作，在四个案例研究中对MultiNet进行设计。该奖项反映了NSF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.