Walktrap Community Detection — community

Detects communities via random walks. Nodes within the same community tend to 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 network weights, NA for unweighted.
steps: Number of random walk steps. Default 4.
merges: Logical; return merge matrix? Default TRUE.
modularity: Logical; return modularity scores? Default TRUE.
membership: Logical; return membership vector? Default TRUE.
...: Additional arguments passed to to_igraph

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

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

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

  # Default 4 steps
  comm <- community_walktrap(g)

  # More steps for larger communities
  comm2 <- community_walktrap(g, steps = 8)
}