community_spinglass: Spinglass Community Detection

In cograph: Analysis and Visualization of Complex Networks

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,
  ...
)

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

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

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)

cograph documentation built on April 1, 2026, 1:07 a.m.