CAREER: General And Optimal Layered Network Visualization

Northeastern University, Boston MA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Human decision-making in fields such as medicine, computer science, physics, and sociology rely on structured data. Networks have long been common models in computer science for structuring data. However, only recently network visualization has become more commonly used tool; for instance, publishers are starting to use visualizations of networks in news articles. Layered network visualizations show either changes in networks over time or flows between states. They help visualize complex dynamics such as how water moves through the parts of a steam engine. Creating an optimal layout based on human readability criteria is an open challenge. Existing algorithms creating visualizations prioritize computational speed over human readability. The goal of this project is to create general and optimal layout algorithms for layered network visualizations that prioritize readability instead. Optimal layouts are particularly beneficial for domain applications where readability is paramount, e.g., in medicine where mistakes can have significant consequences. Through integrated research and education, the project will motivate students to work on meaningful human problems by combining applied and basic research; attract and train the next generation of visualization scientists; and widely disseminate research results to help future researchers and designers create more effective visual data exploration and decision-making tools. This research will advance the state-of-the art in visualizing layered networks and lead to more effective visual data exploration and decision-making tools. It will provide optimal layouts for critical tasks and baselines for evaluating layout heuristics. Five formative studies will produce a corpus of general requirements for layered network visualization which will guide future researchers and practitioners. These requirements will be integrated into a user-customizable optimal layout algorithm. The algorithm, as well as approximate and heuristic layout approaches, will be released in a free and open-source library for layered network visualization. Domain-specific and general computational and human-subjects research will provide evidence-backed design guidelines that are transferable across domains. Experiments will define scalability limitations both in terms of human readability and technical feasibility. This project will contribute to assessing future layout algorithms by providing benchmark datasets, evaluation methodologies, and optimal baseline implementations. Combined applied and basic research studies will lead early-career graduate students to publications and careers working on prosocial problems in cross-disciplinary teams. The project will engage grade 6--12 students in science, technology, engineering, and mathematics through summer camp activities and develop instructional materials for teaching graduate students in courses without prior visualization components. Research outcomes will be publicly disseminated at academic venues, online, and through domain collaborators. To encourage rapid and unrestricted adoption and replicable, reproducible research all products---such as preprints, source code, study materials, and datasets---will be released free and open-source on a reliable long-term archive. 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.