Backbone meaning in networking12/6/2023 ![]() In the sections below, we use randomly generated data like this for toy examples, but also use real data for empirical examples. Therefore, the disparity filter model is recommended, for which additional information is available by typing ?disparity in the R console. Based on these characteristics, it is recognized as a weighted and directed adjacency matrix representing a weighted unipartite network. Here, the source data is a randomly generated 10 × 10 matrix of values between 0 and 1. The backbone.suggest() function can be used to examine the source data, identify the relevant backbone models, and suggest the most appropriate model. Extracting the backbone using one of these functions yields an unweighted unipartite network, thus as the figure illustrates, the user may subsequently extract a second-order backbone using the sparsify() function. For example, when starting with an unweighted bipartite network, a user may extract the backbone from its weighted projection using any of the following models: sdsm(), fdsm(), fixedrow(), fixedcol(), fixedfill(), disparity(), or global(). The relevant backbone models depend on the type of network. The source data may represent an unweighted bipartite network, a weighted unipartite network, or an unweighted unipartite network. A user begins with source data, which can take the form of an R matrix object, sparse Matrix object, an edgelist stored as a dataframe object, or an igraph object. įig 1 illustrates the typical workflow of the backbone package. The code and data necessary to replicate the examples shown in this paper are available at. Additional information about the CRAN distribution is available at, while additional materials relating to backbone, including papers, presentations, workshop materials, and data sets are available at. ![]() It also displays the recommended citation for the package, sources for help using the package, and the command to install the beta release of the backbone package. The startup message displays the version of the backbone package that is installed and has been loaded for use. |_/ Beta: type devtools::install_github(“zpneal/backbone”, ref = “devel”) |#|_) | Help: type vignette(“backbone”) email github zpneal/backbone backbone: An R package to extract network Once installed, the backbone package can be loaded using: The most recent release of the backbone package can be installed in R from the Comprehensive R Archive Network (CRAN) using:Ĭontent type 'application/x-gzip' length 1758582 bytes (1.7 MB) Finally, the sixth section concludes with a discussion of past applications of the backbone package, its limitations, and future directions for the implementation of backbone models. The fifth section reviews issues of statistical inference that arise in backbone models. Each of these sections begin with an overview of the relevant backbone models, then provide a small toy example to illustrate the model, followed by an empirical example to demonstrate its use in practice. The second, third, and fourth sections illustrate how the package can be used to extract the backbone from a weighted network, from a bipartite projection, and from an unweighted network, respectively. The first section provides an overview of the backbone package. Instead, the purpose of this paper is to provide a practical guide to using the backbone package for backbone extraction. The formal mathematical details of these methods, and evidence of their performance as structure-preserving or structure-revealing backbone models, is extensively documented elsewhere and referenced below. The backbone package for R aims to overcome this practical limitation of network backbone extraction by providing an integrated implementation of existing methods. ![]() However, applying these methods in practice has been challenging because they were either not implemented or implemented in different software languages. Many backbone methods have been proposed for extracting a backbone N′ from a network N, with different methods designed for different types of networks, or to preserve different types of structural features. Given a complex network N, which may be weighted or unweighted, its backbone N′ is a sparse and unweighted subgraph that aims to preserve or reveal important structural features. Although their ability to represent complexity can be a virtue, in some cases (e.g., computationally intensive analysis, presence of noise, visualization) it is useful to simplify a network and focus instead on its backbone. Networks are useful for representing phenomena in a broad range of domains.
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