cograph (version 2.0.0)

community_spinglass: Spinglass Community Detection

Description

Statistical mechanics approach using simulated annealing. Can handle negative edge weights.

Usage

community_spinglass(
  x,
  weights = NULL,
  vertex = NULL,
  spins = 25,
  parupdate = FALSE,
  start.temp = 1,
  stop.temp = 0.01,
  cool.fact = 0.99,
  update.rule = c("config", "random", "simple"),
  gamma = 1,
  implementation = c("orig", "neg"),
  gamma.minus = 1,
  seed = NULL,
  ...
)
com_sg(
  x,
  weights = NULL,
  vertex = NULL,
  spins = 25,
  parupdate = FALSE,
  start.temp = 1,
  stop.temp = 0.01,
  cool.fact = 0.99,
  update.rule = c("config", "random", "simple"),
  gamma = 1,
  implementation = c("orig", "neg"),
  gamma.minus = 1,
  seed = NULL,
  ...
)

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

Arguments

x

Network input

weights

Edge weights. NULL uses network weights, NA for unweighted.

vertex

Vertex to find community for (single community mode). NULL for full partitioning.

spins

Number of spins (maximum communities). Default 25.

parupdate

Parallel update mode. Default FALSE.

start.temp

Starting temperature. Default 1.

stop.temp

Stopping temperature. Default 0.01.

cool.fact

Cooling factor. Default 0.99.

update.rule

Update rule: "config" (default), "random", or "simple".

gamma

Gamma parameter for modularity. Default 1.

implementation

"orig" (default) or "neg" (for negative weights).

gamma.minus

Gamma for negative weights in "neg" implementation.

seed

Random seed for reproducibility. Default NULL.

...

Additional arguments passed to to_igraph

References

Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74, 016110.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_spinglass(g)
igraph::membership(comm)
net <- as_cograph(matrix(runif(25), 5, 5))
com_sg(net)