cograph (version 2.0.0)

community_consensus: Consensus Community Detection

Description

Runs a stochastic community detection algorithm multiple times and finds consensus communities via co-occurrence matrix thresholding. This approach produces more robust and stable community assignments than single runs.

Usage

community_consensus(
  x,
  method = c("louvain", "leiden", "infomap", "label_propagation", "spinglass"),
  n_runs = 100,
  threshold = 0.5,
  seed = NULL,
  ...
)
com_consensus(
  x,
  method = c("louvain", "leiden", "infomap", "label_propagation", "spinglass"),
  n_runs = 100,
  threshold = 0.5,
  seed = NULL,
  ...
)

Value

A cograph_communities object with consensus membership.

Arguments

x

Network input: matrix, igraph, network, cograph_network, or tna object

method

Community detection algorithm to use. Default "louvain". Must be a stochastic method (louvain, leiden, infomap, label_propagation, spinglass).

n_runs

Number of times to run the algorithm. Default 100.

threshold

Co-occurrence threshold for consensus. Default 0.5. Nodes that appear together in >= threshold proportion of runs are placed in the same community.

seed

Optional seed for reproducibility. If provided, seeds for individual runs are derived from this seed.

...

Additional arguments passed to the community detection method.

Details

The algorithm works as follows:

1. Run the specified algorithm n_runs times (without seeds to allow variation)

2. Build a co-occurrence matrix counting how often each pair of nodes appears in the same community

3. Normalize to proportions (0-1)

4. Threshold to create a consensus graph (edge if co-occurrence >= threshold)

5. Run walktrap on the consensus graph to get final communities

References

Lancichinetti, A., & Fortunato, S. (2012). Consensus clustering in complex networks. Scientific Reports, 2, 336.

See Also

communities, community_louvain

Examples

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

  # Consensus from 50 Louvain runs
  cc <- community_consensus(g, method = "louvain", n_runs = 50)
  print(cc)

  # Stricter threshold for more robust communities
  cc2 <- community_consensus(g, threshold = 0.7, n_runs = 100)
}