Infomap Community Detection — community

Information-theoretic community detection based on random walks. The partition minimizes the map equation, the description length of a random walk on the network.

Usage

community_infomap(
  x,
  weights = NULL,
  v.weights = NULL,
  nb.trials = 10,
  modularity = TRUE,
  seed = NULL,
  ...
)

com_im(
  x,
  weights = NULL,
  v.weights = NULL,
  nb.trials = 10,
  modularity = TRUE,
  seed = NULL,
  ...
)

Arguments

x: Network input.
weights: Edge weights. NULL uses the network weights and NA runs unweighted. Negative weights are replaced by their absolute values.
v.weights: Vertex weights (teleportation weights).
nb.trials: Number of optimization trials. Default 10.
modularity: Logical. Whether modularity is computed. Default TRUE.
seed: Random seed for reproducibility. Default NULL.
...: 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

Rosvall, M., & Bergstrom, C.T. (2008). Maps of random walks on complex networks reveal community structure. PNAS, 105(4), 1118-1123.

Examples

community_infomap(regulation_net, nb.trials = 10, seed = 1)
#> Community structure (infomap)
#>   Nodes: 10  | Communities: 1  | Modularity: 0 
#>   Sizes: 10 
#> 
#>        node community
#>     Explore         1
#>        Plan         1
#>     Monitor         1
#>       Adapt         1
#>     Reflect         1
#>     Discuss         1
#>  Synthesize         1
#>    Evaluate         1
#>      Create         1
#>       Share         1