Label Propagation Community Detection — community_label

Label propagation community detection. Each node repeatedly adopts the most frequent label among its neighbors.

Usage

community_label_propagation(
  x,
  weights = NULL,
  mode = c("out", "in", "all"),
  initial = NULL,
  fixed = NULL,
  seed = NULL,
  ...
)

com_lp(
  x,
  weights = NULL,
  mode = c("out", "in", "all"),
  initial = NULL,
  fixed = NULL,
  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.
mode: Direction of label propagation in directed graphs, one of "out" (default), "in" or "all".
initial: Initial labels, an integer vector, or NULL for a unique label per node.
fixed: Logical vector marking the nodes whose labels are fixed.
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

Raghavan, U.N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76, 036106.

Examples

community_label_propagation(regulation_net, seed = 1)
#> Community structure (label_propagation)
#>   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