Label Propagation Community Detection

Fast semi-synchronous label propagation algorithm. Each node 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 network weights, NA for unweighted.

mode

For directed graphs: "out" (default), "in", or "all".

initial

Initial labels (integer vector or NULL for unique labels).

fixed

Logical vector indicating which labels are fixed.

seed

Random seed for reproducibility. Default NULL.

...

Additional arguments passed to to_igraph

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

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

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Basic label propagation
  comm <- community_label_propagation(g)

  # With some nodes fixed to specific communities
  initial <- rep(NA, igraph::vcount(g))
  initial[1] <- 1  # Node 1 in community 1
  initial[34] <- 2 # Node 34 in community 2
  fixed <- !is.na(initial)
  initial[is.na(initial)] <- seq_len(sum(is.na(initial)))
  comm2 <- community_label_propagation(g, initial = initial, fixed = fixed)
}
net <- as_cograph(matrix(runif(25), 5, 5))
com_lp(net)
#> Community structure (label_propagation)
#>   Number of communities: 1 
#>   Modularity: 0 
#>   Community sizes: 5 
#>   Nodes: 5