Walktrap Community Detection — community

Detects communities from short random walks. Nodes in the same community have short random walk distances.

Usage

community_walktrap(
  x,
  weights = NULL,
  steps = 4,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

com_wt(
  x,
  weights = NULL,
  steps = 4,
  merges = 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.
steps: Length of the random walks. Default 4.
merges: Logical. Whether igraph stores the merge matrix. 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, whose only other argument is directed; anything else raises an "unused argument" error.

Value

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

References

Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10(2), 191-218.

Examples

community_walktrap(regulation_net, steps = 4)
#> Community structure (walktrap)
#>   Nodes: 10  | Communities: 2  | Modularity: 0.1976 
#>   Sizes: 5, 5 
#> 
#>        node community
#>     Explore         1
#>        Plan         2
#>     Monitor         2
#>       Adapt         1
#>     Reflect         1
#>     Discuss         1
#>  Synthesize         1
#>    Evaluate         2
#>      Create         2
#>       Share         2