cograph (version 2.0.0)

communities: Community Detection

Description

Detects communities/clusters in networks using various algorithms. Provides a unified interface to igraph's community detection functions with full parameter exposure.

Usage

communities(
  x,
  method = c("louvain", "leiden", "fast_greedy", "walktrap", "infomap",
    "label_propagation", "edge_betweenness", "leading_eigenvector", "spinglass",
    "optimal", "fluid"),
  weights = NULL,
  resolution = 1,
  directed = NULL,
  seed = NULL,
  ...
)

Value

A cograph_communities object (extends igraph's communities class) with components:

membership

Integer vector of community assignments

modularity

Modularity score of the partition

algorithm

Name of the algorithm used

names

Node names if available

vcount

Number of nodes

Arguments

x

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

method

Community detection algorithm. One of:

• "louvain" - Louvain modularity optimization (default, fast)

• "leiden" - Leiden algorithm (improved Louvain)

• "fast_greedy" - Fast greedy modularity optimization

• "walktrap" - Random walk-based detection

• "infomap" - Information theoretic approach

• "label_propagation" - Label propagation (very fast)

• "edge_betweenness" - Girvan-Newman algorithm

• "leading_eigenvector" - Leading eigenvector method

• "spinglass" - Spinglass simulation

• "optimal" - Exact modularity optimization (slow)

• "fluid" - Fluid communities algorithm

weights

Edge weights. If NULL, uses edge weights from the network if available, otherwise unweighted. Set to NA for explicitly unweighted.

resolution

Resolution parameter for modularity-based methods (louvain, leiden). Higher values yield more communities. Default 1.

directed

Logical; whether to treat the network as directed. Default NULL (auto-detect).

seed

Random seed for reproducibility. Only applies to stochastic algorithms (louvain, leiden, infomap, label_propagation, spinglass).

...

Additional parameters passed to the specific algorithm. See individual functions for details.

Details

Algorithm Selection Guide:

Algorithm	Best For	Time Complexity
louvain	Large networks, general use	O(n log n)
leiden	Large networks, better quality than louvain	O(n log n)
fast_greedy	Medium networks	O(n² log n)
walktrap	Networks with clear community structure	O(n² log n)
infomap	Directed networks, flow-based	O(E)
label_propagation	Very large networks, speed critical	O(E)
edge_betweenness	Small networks, hierarchical	O(E² n)
leading_eigenvector	Networks with dominant structure	O(n²)
spinglass	Small networks, allows negative weights	O(n³)
optimal	Tiny networks only (<50 nodes)	NP-hard
fluid	When k is known	O(E k)

See Also

community_louvain, community_leiden, community_fast_greedy, community_walktrap, community_infomap, community_label_propagation, community_edge_betweenness, community_leading_eigenvector, community_spinglass, community_optimal, community_fluid

Examples

# Create a network with community structure
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Default (Louvain)
  comm <- cograph::communities(g)
  print(comm)

  # Walktrap
  comm2 <- cograph::communities(g, method = "walktrap")
  print(comm2)
}