# cluster_infomap

From igraph v1.0.0
by Gabor Csardi

##### Infomap community finding

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

- Keywords
- graphs

##### Usage

```
cluster_infomap(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 edges are consi`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 all vertices`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

`cluster_infomap`

returns a`communities`

object, please see the`communities`

manual page for details.

##### 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 <- make_graph("Zachary")
imc <- cluster_infomap(g)
membership(imc)
communities(imc)
```

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

### Community examples

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