What is a network backbone?
Networks are often complex and contain a lot of information. In some cases, networks are weighted, where the weighted capture the strength of a friendship, or the capacity of a road. In other cases, networks may be very dense, which obscures underlying patterns. Because weighted and dense networks contain a lot of information, they can be difficult to analyze and visualize. Therefore, it is often helpful to focus on the backbone, which is an unweighted network that contains only the most significant links.
The R backbone package
The backbone package for R makes it easy to identify the most significant links in a network and to extract its backbone. The package is freely available from CRAN and can be installed by typing install.packages("backbone") into the R command line. Once the package is installed, load it by typing library(backbone) and view the documentation by typing ?backbone or vignette("backbone"). You can find a short walk-thru here, and a longer tutorial here. If you find a bug or want to request a few feature, please let us know via GitHub. For help using the package, don't hesitate to contact me.
The models in the backbone package have been used to study political coalitions and polarization, gastrointestinal dysbiosis in zebrafish, X-men collaborations, and Christmas movies. If you have used backbone in a publication or pre-print, let me know and we'll send you a cool backbone hex decal!
What can backbone do?
The backbone package can extract backbones from weighted networks, bipartite projections, and unweighted networks. Here are some examples of the backbones that can be extracted from each type of network using different functions from the package:
Papers about backbone
for more, please visit this page
Neal, Z. P. (2022). backbone: An R package to extract network backbones. PLoS ONE 17, e0269137. https://doi.org/10.1371/journal.pone.0269137
Neal, Z. P., Domagalski, R., & Sagan, B. (2021). Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections. Scientific Reports, 11, 23929. https://doi.org/10.1038/s41598-021-03238-3
Neal, Z. P., Domagalski, R., & Sagan, B. (2021). Analysis of spatial networks from bipartite projections using the R backbone package. Geographical Analysis. https://doi.org/10.1111/gean.12275 [pre-print]
Domagalski, R., Neal, Z. P., & Sagan, B. (2021). backbone: An R package for backbone extraction of weighted graphs. PLoS ONE, 16, e0244363. https://doi.org/10.1371/journal.pone.0244363
Neal, Z. P. (2014). The backbone of bipartite networks: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Social Networks, 39, 84 – 97. https://doi.org/10.1016/j.socnet.2014.06.001 [pre-print]
Collaborators on backbone
Research on backbone has been supported by the National Science Foundation (#1851625, #2016320, & #2211744) and is a collaboration with Dr. Bruce Sagan and Rachel Domagalski. The project has also benefited enormously from conversations with David Schoch, Giovanni Strona, Bruce Desmarais, and many others.