Girvan-Newman algorithm. Iteratively removes edges with highest betweenness centrality to reveal community structure.
Usage
community_edge_betweenness(
x,
weights = NULL,
directed = TRUE,
edge.betweenness = TRUE,
merges = TRUE,
bridges = TRUE,
modularity = TRUE,
membership = TRUE,
...
)
com_eb(
x,
weights = NULL,
directed = TRUE,
edge.betweenness = TRUE,
merges = TRUE,
bridges = TRUE,
modularity = TRUE,
membership = TRUE,
...
)Arguments
- x
Network input
- weights
Edge weights. NULL uses network weights, NA for unweighted.
- directed
Logical; treat graph as directed? Default TRUE.
- edge.betweenness
Logical; return edge betweenness values? Default TRUE.
- merges
Logical; return merge matrix? Default TRUE.
- bridges
Logical; return bridge edges? Default TRUE.
- modularity
Logical; return modularity scores? Default TRUE.
- membership
Logical; return membership vector? Default TRUE.
- ...
Additional arguments passed to
to_igraph
References
Girvan, M., & Newman, M.E.J. (2002). Community structure in social and biological networks. PNAS, 99(12), 7821-7826.
Examples
g <- igraph::make_graph("Zachary")
comm <- community_edge_betweenness(g)
membership(comm)
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
#> 1 1 2 1 3 3 3 1 4 5 3 1 1 1 4 4 3 1 4 1 4 1 4 4 2 2
#> 27 28 29 30 31 32 33 34
#> 4 2 2 4 4 2 4 4
net <- as_cograph(matrix(runif(25), 5, 5))
com_eb(net)
#> Warning: Membership vector will be selected based on the highest modularity score.
#> Source: community/edge_betweenness.c:503
#> Community structure (edge_betweenness)
#> Nodes: 5 | Communities: 5 | Modularity: 0.0244
#> Sizes: 1, 1, 1, 1, 1
#>
#> node community
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5