Abstract
Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone,' an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality.
Highlights
Networks are useful for studying many different phenomena in the natural and social worlds, but network data can be difficult to collect directly
We introduce and demonstrate version 1.2.2 of the backbone R package, which implements four backbone extraction methods—a universal threshold, a hypergeometric model, a stochastic degree sequence model, and a fixed degree sequence model—in a common framework that facilitates their use
We have presented four methods—universal threshold, Hypergeometric Model (HM), Stochastic Degree Sequence Model (SDSM), and Fixed Degree Sequence Model (FDSM)—for identifying significant links in a weighted bipartite projection, and for extracting its binary or signed backbone
Summary
Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; we enable the publication of all of the content of peer review and author responses alongside final, published articles. Data Availability Statement: The data and code necessary to replicate the examples in this paper are available at https://osf.io/myje5/.
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