![]() The functions in the backbone package support many different dataįormats that can be used to represent a network or graph in R: Includes several utility functions that are usedīy the backbone methods, but may also have independent applications. The sections below describe and illustrate the backbone functionsĪpplicable to different types of networks, including weighted bipartite projections, non-projection weighted networks, and unweighted networks. ![]() Proceedings of the National Academy of Sciences, 106(16), 6483-6488. Extracting the multiscale backbone of complex weighted networks. backbone: An R Package to Extract Network Backbones. This reduced the number of edges by 100%, and reduced the number of connected nodes by 100%. An edge was retained in the backbone if its weight was statistically significant (alpha = 0.05) using the disparity filter (Serrano et al., 2009). backbone Extracting backbone using: disparity(G, alpha = 0.05, signed = FALSE, mtc = "none", class = "original", narrative = TRUE) #> #> = Suggested manuscript text and citations = #> We used the backbone package for R (v2.1.2 Neal, 2022) to extract the unweighted backbone of a weighted and directed unipartite network containing 10 nodes. S can range from 0 to 1, with smaller values yieldingĭat The disparity filter is suggested. Then the function will extract the backbone using the suggested approachĪnd will display text describing what it did. Specify a significance level or sparsification parameter s, ![]() Helper function will examine the data and make a suggestion. If you are unsure about your type of network data or which backboneįunction to use, you can run suggest.backbone(dat). Of network data, and highlights in blue the analytic workflow andįunction that is recommended for each type. The figure illustrates which functions are applicable for which types Sdsm(dat)), which yields an unweighted network of the same You run an applicable backbone function on these data (e.g., Sparse Matrix object), an edge list (as a 2- or 3-column matrix or data You begin with some network data dat, which may takeĪny of the following forms: an adjacency matrix (as a matrix, Matrix, or Different types of networks require different backboneĮxtraction methods, however the basic workflow using the backbone Is, identifying and preserving only the most important edges in a The primary use of the backbone package is backbone extraction, that
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |