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
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)
#> [1] 4 4 4 4 1 1 1 4 3 4 1 4 4 4 3 3 1 4 3 4 3 4 3 3 2 2 3 3 2 3 3 2 3 3
net <- as_cograph(matrix(runif(25), 5, 5))
com_sg(net)
#> Community structure (spinglass)
#> Number of communities: 2
#> Modularity: 0.0362
#> Community sizes: 2, 3
#> Nodes: 5