Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data, Interaction, Visualization, and Software (MLN-DIVE)

合作研究：CCRI：规划：数据、交互、可视化和软件的多层网络 (MLN) 社区基础设施 (MLN-DIVE)

• 批准号: 2120361
• 负责人: Kamesh Madduri
• 金额: $ 3万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120361&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; Planning; Multilayer

A multilayer network (MLN) is a powerful and expressive mathematical tool for modeling and analyzing social, economic, biological, and technological systems. Informally, a multilayer network is a collection of related graphs. Applications of multilayer networks include understanding social networks, economic systems, online marketplaces, and detecting vulnerabilities in cyber-physical systems. While this research area is rapidly growing, there is a dearth of a community infrastructure for researchers, developers, and end users to share, participate, and use latest tools and algorithms. This planning project will collect community infrastructure requirements for supporting the MLN community. It will also develop preliminary visualization and drill down analysis tools.This project uses a formally established network decoupling approach to perform various aggregate analysis (community, centrality, substructure detection, etc.) using individual layers and composing them. This approach has also been shown to be efficient compared to the same analysis without using the decoupling approach. Network decoupling seeks to address issues that are critical for multilayer analysis, such as reducing information loss and preserving structural and semantic information.The broader impact of this planning project is to provide meaningful and appropriate analysis tools that are grounded in theory to a broad range of applications from different domains. The focus is on facilitating the mainstream use of multilayer network analysis in data analysis, research and teaching. GUI-based dashboards and drill down analysis will be developed for broader usage of the tools developed.This collaborative project brings together investigators from The University of Texas at Arlington (UTA), University of North Texas (UNT), and Pennsylvania State University (PSU) to develop efficient and scalable algorithms/approaches, to provide a portal for accessing data sets and computations on MLNs, and an interchange for the community to participate and discuss infrastructure needs on a broader scale.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.

多层网络 (MLN) 是一种强大且富有表现力的数学工具，用于建模和分析社会、经济、生物和技术系统。非正式地，多层网络是相关图的集合。多层网络的应用包括理解社交网络、经济系统、在线市场以及检测网络物理系统中的漏洞。尽管这一研究领域正在快速发展，但仍缺乏供研究人员、开发人员和最终用户共享、参与和使用最新工具和算法的社区基础设施。该规划项目将收集支持 MLN 社区的社区基础设施要求。它还将开发初步的可视化和下钻分析工具。该项目使用正式建立的网络解耦方法，使用各个层并组合它们来执行各种聚合分析（社区、中心性、子结构检测等）。与不使用解耦方法的相同分析相比，这种方法也被证明是有效的。网络解耦旨在解决对多层分析至关重要的问题，例如减少信息丢失以及保留结构和语义信息。该规划项目的更广泛影响是提供有意义且适当的分析工具，这些工具以理论为基础，适用于广泛的领域。来自不同领域的应用程序。重点是促进多层网络分析在数据分析、研究和教学中的主流使用。将开发基于 GUI 的仪表板和深入分析，以便更广泛地使用所开发的工具。该合作项目汇集了来自德克萨斯大学阿灵顿分校 (UTA)、北德克萨斯大学 (UNT) 和宾夕法尼亚州立大学 (PSU) 的研究人员）开发高效且可扩展的算法/方法，提供访问 MLN 上的数据集和计算的门户，以及社区在更广泛的范围内参与和讨论基础设施需求的交换平台。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。