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

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

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.

`cluster_infomap`

returns a `communities`

object,
please see the `communities`

manual page for details.

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

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,
http://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,
http://arxiv.org/abs/0906.1405.

Other community finding methods and `communities`

.

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

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