# infomap.community

From igraph v0.6.5-2
by Gabor Csardi

##### Infomap community finding

Find community structure that minimizes the expected description length of a random walker trajectory

- Keywords
- graphs

##### Usage

```
infomap.community (graph, e.weights = NULL, v.weights = NULL,
nb.trials = 10, modularity = TRUE)
```

##### Arguments

- graph
- The input graph.
- e.weights
- If not
`NULL`

, then a numeric vector of edge weights. The length must match the number of edges in the graph. By default the edge attribute is used as weights. If it is not present, then all edge`weight`

- v.weights
- If not
`NULL`

, then a numeric vector of vertex weights. The length must match the number of vertices in the graph. By default the vertex attribute is used as weights. If it is not present, then a`weight`

- nb.trials
- The number of attempts to partition the network (can be any integer value equal or larger than 1).
- modularity
- Logical scalar, whether to calculate the modularity score of the detected community structure.

##### Details

Please see the details of this method in the references given below.

##### Value

`infomap.community`

returns a`communities`

object, please see the`communities`

manual page for details.

##### concept

Community structure

##### References

The original paper: M. Rosvall and C. T. Bergstrom, Maps of
information flow reveal community structure in complex networks,
*PNAS* 105, 1118 (2008)

A more detailed paper: M. Rosvall, D. Axelsson, and C. T. Bergstrom,
The map equation, *Eur. Phys. J. Special Topics* 178, 13 (2009).

##### See Also

Other community finding methods and `communities`

.

##### Examples

```
## Zachary's karate club
g <- graph.famous("Zachary")
imc <- infomap.community(g)
membership(imc)
communities(imc)
```

*Documentation reproduced from package igraph, version 0.6.5-2, License: GPL (>= 2)*

### Community examples

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