Overlap between the strongest edges of several measures

For each pair of measures, the Jaccard index between the sets of their strongest edges (the top share of the edges with the highest weights). Correlations summarise all the edges, but a network analysis often relies on the strongest ones (after a filtering of the weak ties). This function tells how many of the strongest ties two measures have in common: 1 means the same strongest edges, 0 none in common.

Usage

overlap_top_weights(edges, top = 0.1)

Arguments

edges

A named list of at least two edge tables, for instance from compute_couplings().

top

Share of edges kept for each measure, between 0 and 1 (0.1 by default: the top 10% of the edges). Ties at the threshold are all kept.

Value

A symmetric matrix of Jaccard indices, with the number of edges kept by each measure in the attribute "n_top".

Details

See also

Other tools to compare coupling measures: compute_couplings(), correlate_weights(), merge_weights(), plot_weights(), summarise_weights()

Examples

library(biblionetwork)
edges <- compute_couplings(Ref_stagflation, "Citing_ItemID_Ref", "ItemID_Ref",
                           weight_threshold = 2)
overlap_top_weights(edges, top = 0.1)
#>                angle  strength similarity   jaccard   vladutz
#> angle      1.0000000 0.3853211  0.7660819 0.7869822 0.4046512
#> strength   0.3853211 1.0000000  0.4519231 0.3727273 0.3482143
#> similarity 0.7660819 0.4519231  1.0000000 0.6593407 0.5024876
#> jaccard    0.7869822 0.3727273  0.6593407 1.0000000 0.3789954
#> vladutz    0.4046512 0.3482143  0.5024876 0.3789954 1.0000000
#> attr(,"n_top")
#>      angle   strength similarity    jaccard    vladutz 
#>        151        151        151        151        151