Extracting the backbone of weighted networks

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In this project, methods to extract the backbone of weighted networks are investigated and computer software to use these methods is developed. Social networks are often complex, with the interactions between actors (e.g., people, organizations, cities) varying in strength, which can be represented by weighted networks. Because weighted networks are challenging to analyze and visualize, it is often useful to focus on their backbones, which contain only the most significant connections. Many approaches to backbone extraction exist, but we know little about whether they work or how to choose one approach over another. By providing guidance on and tools for network backbone extraction, this project will enable researchers to analyze better information-rich network data in a wide range of socially significant contexts, and to communicate their findings more easily to diverse audiences through visualization. The goal of this project is to facilitate researchers’ ability to correctly extract the backbone of weighted networks. Achieving this goal involves six activities. First, bipartite ensemble methods are refined to make them faster and more flexible. Second, the R backbone package is extended to allow the extraction of backbones from all types of weighted networks, to accommodate larger datasets, and to be interoperable with other network analysis packages. Third, backbone methods are compared empirically using benchmark datasets to explore their similarities and differences in practice. Fourth, backbone methods are compared analytically to determine their computational complexities and the functional form of their null edge weight distributions. Fifth, backbone methods are compared numerically using synthetic weighted network data, allowing identification of the conditions under which each method validly reproduces a known ground truth. Finally, training materials are developed to instruct researchers on the selection of backbone methods and on the use of the backbone package for their extraction.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.