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

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.