plot_disparity_filter_analysis: Visual analysis of the disparity filter applied to an...

In galanisl/DisparityFilter: Disparity filter for complex undirected weighted networks

Description

After the application of function analyse_disparity_filter to an undirected weighted network, it generates three ggplots showing how the remaining fraction of nodes changes as a function of the remaining fraction of links and total weight as more stringent filters are applied to the network. The third plot shows how the fraction of nodes in the largest connected component of the network changes as a function of the different filters applied. All three plots indicate the final situation of the network for the recommended disparity filter.

Usage

plot_disparity_filter_analysis(disp_analysis)

Arguments

disp_analysis

data frame; The data frame resulting from the application of function analyse_disparity_filter.

Value

A list of five ggplots:

LvsN	The remaining fraction of nodes as a function of the remaining fraction of links.
WvsN	The remaining fraction of nodes as a function of the remaining fraction of total weight.
AvsLCC_tot	Fraction of nodes from the original network in the LCC of the filtered network as a function of different p-value thresholds.
AvsLCC_bb	Fraction of nodes from the filtered network in the LCC of the filtered network as a function of different p-value thresholds.
AvsCC	Clustering coefficient as a function of different p-value thresholds.

Author(s)

Gregorio Alanis-Lobato [e-mail]

References

Serrano, M. A. et al. (2009) Extracting the multiscale backbone of complex weighted networks. PNAS 106(16).

Garcia-Perez, G. et al. (2016) The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013. Scientific Reports 6(33441).

Examples

# Get disparity p-values for the edges of the included US Airports network
air_with_pval <- get_edge_disparity_pvals(net = air)
# Analyse the topology of the networks resulting from the application of 
# different disparity filters
analysis <- analyse_disparity_filter(net = air_with_pval, breaks = 100)
# Plot the results of the analysis
p <- plot_disparity_filter_analysis(disp_analysis = analysis)

galanisl/DisparityFilter documentation built on May 16, 2019, 5:36 p.m.