R/measure_centrality_between.R
measure_central_tie_between.Rdtie_by_betweenness() measures the number of shortest paths going through a tie.
All measures attempt to use as much information as they are offered,
including whether the networks are directed, weighted, or multimodal.
If this would produce unintended results,
first transform the salient properties using e.g. to_undirected() functions.
All centrality and centralization measures return normalized measures by default,
including for two-mode networks.
tie_by_betweenness(.data, normalized = TRUE)A network object of class stocnet, igraph, tbl_graph, network, or similar.
Internally any of these will be coerced to an efficient implementation.
For more information on possible coercions, see e.g. manynet::as_stocnet().
Logical scalar, whether scores are normalized. Different denominators may be used depending on the measure, whether the object is one-mode or two-mode, and other arguments. By default TRUE.
A tie_measure numeric vector the length of the ties in the network,
providing the scores for each tie.
If the network is labelled,
then the scores will be labelled with the ties' adjacent nodes' names.
The object also carries the measure it computed, the range its values
can fall within, and whether and how those values were normalized.
These are shown as a one-line header when the object is printed.
Where a measure offers a choice between several ways of counting the
same thing, it also carries the variant it used.
All can be retrieved with attr().
The betweenness centrality of a tie, also known as edge betweenness,
counts the shortest paths between other nodes that run along it.
It is best known as the quantity iteratively recomputed by the
Girvan-Newman community detection algorithm, where the ties with the
highest betweenness are removed first; see node_in_betweenness().
Girvan, Michelle, and Mark E.J. Newman. 2002. "Community structure in social and biological networks". Proceedings of the National Academy of Sciences 99(12): 7821-7826. doi:10.1073/pnas.122653799
Brandes, Ulrik. 2001. "A faster algorithm for betweenness centrality". Journal of Mathematical Sociology 25(2): 163-177. doi:10.1080/0022250X.2001.9990249
Other betweenness:
measure_central_between,
measure_centralisation_between
Other centrality:
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen,
measure_centralisation_between,
measure_centralisation_close,
measure_centralisation_degree,
measure_centralisation_eigen
Other measures:
measure_assort_net,
measure_assort_node,
measure_breadth,
measure_broker_node,
measure_broker_tie,
measure_brokerage,
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen,
measure_closure,
measure_closure_node,
measure_cohesion,
measure_core,
measure_diffusion_infection,
measure_diffusion_net,
measure_diffusion_node,
measure_diverse_net,
measure_diverse_node,
measure_features,
measure_fit,
measure_fragmentation,
measure_hierarchy,
measure_periods
Other tie:
mark_dyads,
mark_select_tie,
mark_ties,
mark_triangles,
measure_broker_tie,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen
(tb <- tie_by_betweenness(ison_adolescents))
#> # Betweenness centrality, normalized [0, 1]
#> `Betty-Sue` `Sue-Alice` `Alice-Jane` `Sue-Dale` `Alice-Dale` `Jane-Dale`
#> 1 0.25 0.107 0.179 0.161 0.089 0.071
#> # ... and 4 more values from this nodeset. Use `print_all(...)` to print all values.
ison_adolescents |> mutate_ties(weight = tb)
#>
#> ── # The Adolescent Society ────────────────────────────────────────────────────
#> # A labelled, weighted, undirected network of 8 adolescents and 10 friendship
#> ties
#>
#> ── Nodes
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#>
#> ── Ties
#> # A tibble: 10 × 3
#> from to weight
#> <int> <int> <tie_mesr>
#> 1 1 2 0.25000000
#> 2 2 3 0.10714286
#> 3 3 4 0.17857143
#> 4 2 5 0.16071429
#> 5 3 5 0.08928571
#> 6 4 5 0.07142857
#> # ℹ 4 more rows
#>