![]() 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 ![]()
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