ABSTRACT

Networks are all around us in many forms, ranging from online social networks to public transportation networks to gene networks in biology. Most networks change over time and are often called temporal or dynamic networks. In this project, a framework for modeling and analyzing dynamic networks that change continuously over time will be developed, even though the networks may only be periodically observed. This framework advances the interdisciplinary field of network science along with the computer and information sciences by developing models to separate the underlying dynamics of the networks from the times at which the networks are observed. The framework can be applied to analyze dynamic network data in many scientific disciplines and in public health applications, including networks of face-to-face interactions between people, which can help scientists better understand the spread of infectious diseases such as COVID-19. This project advances education in network science by creating a curriculum for instruction of dynamic networks at the undergraduate and graduate levels. The project also trains new graduate and undergraduate students, including female students from the University of Toledo's ACM-W chapter, in interdisciplinary data science research. Finally, the project develops and integrates methods for analyzing dynamic networks into the open-source DyNetworkX Python package to reach others who could use them in impactful ways.

Temporal dynamics in networks are known to provide crucial information about the underlying complex systems being modeled by the networks. While significant advances have been made towards understanding the structure of static networks, dynamics are usually incorporated in an ad-hoc manner by creating discrete time snapshots aggregated over some arbitrary time period, primarily for convenience of analysis. The goal of this project is to develop a unified framework for model-based analysis of dynamic networks using continuous-time models that can be applied to both discrete- and continuous-time dynamic network data. Towards this goal, the research team will target five specific aims: 1) learning continuous-time network models from aggregated counts of relational events over time, 2) creating Hawkes process-based generative models for timestamped events with durations, 3) developing kernel smoothing approaches for analyzing dynamic networks, 4) modeling different types of measurement error in dynamic network data, and 5) creating time- and memory-efficient dynamic graph data structures to enable analysis of large dynamic networks with high temporal resolution. Dynamics of networks are given minimal coverage in current network science curricula and textbooks. The model-based analysis techniques to be developed in this project build upon fundamental network theory and empirical observations about real networks and are thus ideal for integration into a typical graduate or undergraduate network science course. The investigator will develop a publicly-available curriculum for instruction on dynamic network representations, models, and analysis methods. The results of this project will provide a glimpse of the possibilities enabled by continuous-time network models and guide future research and education efforts on dynamic networks.

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.

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