# cluster_spinglass

##### Finding communities in graphs based on statistical meachanics

This function tries to find communities in graphs via a spin-glass model and simulated annealing.

- Keywords
- graphs

##### Usage

```
cluster_spinglass(graph, 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)
```

##### Arguments

- graph
- The input graph, can be directed but the direction of the edges is neglected.
- weights
- The weights of the edges. Either a numeric vector or
`NULL`

. If it is null and the input graph has aweight edge attribute then that will be used. If`NULL`

and no such attribute is present then the edges will have e - vertex
- This parameter can be used to calculate the community of a given vertex without calculating all communities. Note that if this argument is present then some other arguments are ignored.
- spins
- Integer constant, the number of spins to use. This is the upper limit for the number of communities. It is not a problem to supply a (reasonably) big number here, in which case some spin states will be unpopulated.
- parupdate
- Logical constant, whether to update the spins of the
vertices in parallel (synchronously) or not. This argument is ignored if the
second form of the function is used (ie. the
argument is present). It is also not implem`vertex`

- start.temp
- Real constant, the start temperature. This argument is
ignored if the second form of the function is used (ie. the
argument is present).`vertex`

- stop.temp
- Real constant, the stop temperature. The simulation
terminates if the temperature lowers below this level. This argument is
ignored if the second form of the function is used (ie. the
argument is present).`vertex`

- cool.fact
- Cooling factor for the simulated annealing. This argument
is ignored if the second form of the function is used (ie. the
argument is present).`vertex`

- update.rule
- Character constant giving the
null-model of the simulation. Possible values:simple andconfig .simple uses a random graph with the same number of edges as the baseline probability and - gamma
- Real constant, the gamma argument of the algorithm. This specifies the balance between the importance of present and non-present edges in a community. Roughly, a comunity is a set of vertices having many edges inside the community and few edges outside th
- implementation
- Character scalar. Currently igraph contains two implementations for the Spin-glass community finding algorithm. The faster original implementation is the default. The other implementation, that takes into account negative weights, can be chosen by supplyi
- gamma.minus
- Real constant, the gamma.minus parameter of the algorithm. This specifies the balance between the importance of present and non-present negative weighted edges in a community. Smaller values of gamma.minus, leads to communities with lesser negative intra-

##### Details

This function tries to find communities in a graph. A community is a set of nodes with many edges inside the community and few edges between outside it (i.e. between the community itself and the rest of the graph.)

This idea is reversed for edges having a negative weight, ie. few negative
edges inside a community and many negative edges between communities. Note
that only the

The `spinglass.cummunity`

function can solve two problems related to
community detection. If the `vertex`

argument is not given (or it is
`NULL`

), then the regular community detection problem is solved
(approximately), i.e. partitioning the vertices into communities, by
optimizing the an energy function.

If the `vertex`

argument is given and it is not `NULL`

, then it
must be a vertex id, and the same energy function is used to find the
community of the the given vertex. See also the examples below.

##### Value

- If the
`vertex`

argument is not given, ie. the first form is used then a`cluster_spinglass`

returns a`communities`

object.If the

`vertex`

argument is present, ie. the second form is used then a named list is returned with the following components: community Numeric vector giving the ids of the vertices in the same community as `vertex`

.cohesion The cohesion score of the result, see references. adhesion The adhesion score of the result, see references. inner.links The number of edges within the community of `vertex`

.outer.links The number of edges between the community of `vertex`

and the rest of the graph.

##### References

J. Reichardt and S. Bornholdt: Statistical Mechanics of
Community Detection, *Phys. Rev. E*, 74, 016110 (2006),

M. E. J. Newman and M. Girvan: Finding and evaluating community structure in
networks, *Phys. Rev. E* 69, 026113 (2004)

V.A. Traag and Jeroen Bruggeman: Community detection in networks with
positive and negative links,

##### See Also

##### Examples

```
g <- sample_gnp(10, 5/10) %du% sample_gnp(9, 5/9)
g <- add_edges(g, c(1, 12))
g <- induced_subgraph(g, subcomponent(g, 1))
cluster_spinglass(g, spins=2)
cluster_spinglass(g, vertex=1)
```

*Documentation reproduced from package igraph, version 1.0.0, License: GPL (>= 2)*