Edge Betweenness Network

Computes the edge betweenness of every existing edge in an htna network and returns a copy whose $weights slot stores those betweenness scores instead of the original transition weights. The actor partition is preserved, so the result can be plotted with plot_htna() to visualise which edges carry the most shortest-path traffic.

Usage

edge_betweenness_htna(x, directed = TRUE, invert = TRUE)

Arguments

x

An htna network from build_htna().

directed

If TRUE (default), shortest paths follow edge direction.

invert

If TRUE (default), use 1 / weight as the edge cost when computing shortest paths.

Value

A copy of x whose $weights matrix entries are edge-betweenness scores at every position where the original network had a non-zero transition. Class is c("htna_edge_betweenness", class(x)).

Details

Mirrors tna::betweenness_network(): edge weights are inverted to costs by default (invert = TRUE) – in transition networks a larger transition probability means the edge is "easier" and so the equivalent path cost is smaller.

See also

centralities_htna() for node-level centrality measures, tna::betweenness_network() for the tna equivalent.

Examples

# \donttest{
data(human_ai)
net <- build_htna(human_ai, actor_type = "actor_type")
#> Warning: A network with one long sequence is not recommended and can't be validated using bootstrap and other confirmatory testings.
#> Metadata aggregated per session: ties resolved by first occurrence in 'session_date' (1 sessions), 'cluster' (42 sessions), 'actor_type' (24 sessions)
eb  <- edge_betweenness_htna(net)
plot_htna(eb)                 # edge thickness = betweenness score

# }