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Edge Betweenness Community Detection

Source: R/communities.R
community_edge_betweenness.Rd

The Girvan-Newman algorithm. It repeatedly removes the edge with the highest edge betweenness and keeps the partition with the highest modularity. On a weighted graph igraph warns that the membership is selected by modularity.

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 the network weights and NA runs unweighted. Negative weights are replaced by their absolute values.

directed

Logical. Whether edge directions are used. Default TRUE. NULL uses the direction of the network.

edge.betweenness

Logical. Whether igraph stores the edge betweenness values. Default TRUE.

merges

Logical. Whether igraph stores the merge matrix. Default TRUE.

bridges

Logical. Whether igraph stores the bridge edges. Default TRUE.

modularity

Logical. Whether igraph stores the modularity scores. Default TRUE.

membership

Logical. Whether igraph computes the membership vector. Default TRUE.

...

Passed to to_igraph. Its only other argument, directed, is already taken by this function, so any further argument raises an "unused argument" error.

Value

A cograph_communities data frame with columns node and community. See communities for its attributes.

References

Girvan, M., & Newman, M.E.J. (2002). Community structure in social and biological networks. PNAS, 99(12), 7821-7826.

Examples

community_edge_betweenness(igraph::make_graph("Zachary"))
#> Community structure (edge_betweenness)
#>   Nodes: 34  | Communities: 5  | Modularity: 0.4013 
#>   Sizes: 10, 6, 5, 12, 1 
#> 
#>  node community
#>     1         1
#>     2         1
#>     3         2
#>     4         1
#>     5         3
#>     6         3
#>     7         3
#>     8         1
#>     9         4
#>    10         5
#>    11         3
#>    12         1
#>    13         1
#>    14         1
#>    15         4
#>    16         4
#>    17         3
#>    18         1
#>    19         4
#>    20         1
#>    21         4
#>    22         1
#>    23         4
#>    24         4
#>    25         2
#>    26         2
#>    27         4
#>    28         2
#>    29         2
#>    30         4
#>    31         4
#>    32         2
#>    33         4
#>    34         4