Leiden Community Detection — community

Leiden algorithm - an improved version of Louvain that guarantees well-connected communities. Supports CPM and modularity objectives.

Usage

community_leiden(
  x,
  weights = NULL,
  resolution = 1,
  objective_function = c("CPM", "modularity"),
  beta = 0.01,
  initial_membership = NULL,
  n_iterations = 2,
  vertex_weights = NULL,
  seed = NULL,
  ...
)

com_ld(
  x,
  weights = NULL,
  resolution = 1,
  objective_function = c("CPM", "modularity"),
  beta = 0.01,
  initial_membership = NULL,
  n_iterations = 2,
  vertex_weights = NULL,
  seed = NULL,
  ...
)

Arguments

x: Network input
weights: Edge weights. NULL uses network weights, NA for unweighted.
resolution: Resolution parameter. Default 1.
objective_function: Optimization objective: "CPM" (Constant Potts Model) or "modularity". Default "CPM".
beta: Parameter for randomness in refinement step. Default 0.01.
initial_membership: Initial community assignments (optional).
n_iterations: Number of iterations. Default 2. Use -1 for convergence.
vertex_weights: Vertex weights for CPM objective.
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

Traag, V.A., Waltman, L., & van Eck, N.J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9, 5233.

Examples

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

  # Standard Leiden
  comm <- community_leiden(g)

  # Higher resolution for more communities
  comm2 <- community_leiden(g, resolution = 1.5)

  # Modularity objective
  comm3 <- community_leiden(g, objective_function = "modularity")
}