# cluster_infomap

##### 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 ‘`weight`

’ edge attribute is used as weights. If it is not present, then all edges are considered to have the same weight. Larger edge weights correspond to stronger connections.- 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 ‘`weight`

’ vertex attribute is used as weights. If it is not present, then all vertices are considered to have the same weight. A larger vertex weight means a larger probability that the random surfer jumps to that vertex.- 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) http://dx.doi.org/10.1073/pnas.0706851105,
https://arxiv.org/abs/0707.0609

A more detailed paper: M. Rosvall, D. Axelsson, and C. T. Bergstrom, The map
equation, *Eur. Phys. J. Special Topics* 178, 13 (2009).
http://dx.doi.org/10.1140/epjst/e2010-01179-1,
https://arxiv.org/abs/0906.1405.

##### See Also

Other community finding methods and `communities`

.

##### Examples

```
# NOT RUN {
## Zachary's karate club
g <- make_graph("Zachary")
imc <- cluster_infomap(g)
membership(imc)
communities(imc)
# }
```

*Documentation reproduced from package igraph, version 1.2.6, License: GPL (>= 2)*