backbone: Extract the backbone of a weighted network using the...

In alessandrobessi/disparityfilter: Disparity Filter Algorithm for Weighted Networks

Description

Given a weighted graph, backbone identifies the 'backbone structure' of the graph, using the disparity filter algorithm by Serrano et al. (2009).

Usage

backbone(graph, weights = E(graph)$weight, directed = is_directed(graph),
  alpha = 0.05)

Arguments

graph	The input graph.
weights	A numeric vector of edge weights, which defaults to E(graph)$weight.
directed	The directedness of the graph, which defaults to the result of is_directed.
alpha	The significance level under which to preserve the edges, which defaults to 0.05.

Value

An edge list corresponding to the 'backbone' of the graph, i.e. the edges of the initial graph that were preserved by the null model that the disparity filter algorithm implements.

Author(s)

Serrano et al. (2009); R implementation by Alessandro Bessi and Francois Briatte

References

Serrano, M.A., Boguna, M. and Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences 106, 6483-6488.

Examples

# undirected network
g <- sample_pa(n = 250, m = 5, directed = FALSE)
E(g)$weight <- sample(1:25, ecount(g), replace = TRUE)
backbone(g)

# directed network
g <- sample_pa(n = 250, m = 5, directed = TRUE)
E(g)$weight <- sample(1:25, ecount(g), replace = TRUE)
backbone(g)

alessandrobessi/disparityfilter documentation built on May 11, 2019, 11:25 p.m.