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

多层网络(MLN)是对社会、经济、生物和技术系统进行建模和分析的一种强大且富有表现力的数学工具。非正式地说，多层网络是相关图形的集合。多层网络的应用包括了解社交网络、经济系统、在线市场，以及检测网络物理系统中的漏洞。虽然这一研究领域正在迅速增长，但研究人员、开发人员和最终用户缺乏社区基础设施来共享、参与和使用最新的工具和算法。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深入分析工具。该项目使用正式建立的网络解耦方法来执行各种聚合分析(社区、中心性、子结构检测等)。使用单独的层并合成它们。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，如减少信息损失和保存结构和语义信息。该规划项目的更广泛影响是为来自不同领域的广泛应用提供有意义和适当的分析工具，这些工具在理论上是基础。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。该合作项目汇集了德克萨斯大学阿灵顿分校(UTA)、北得克萨斯大学(UNT)和宾夕法尼亚州立大学(PSU)的研究人员，以开发高效且可扩展的算法/方法，为访问MLN上的数据集和计算提供门户，并为社区参与和讨论更广泛的基础设施需求提供交流。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。

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.