# cluster_walktrap

##### Community strucure via short random walks

This function tries to find densely connected subgraphs, also called communities in a graph via random walks. The idea is that short random walks tend to stay in the same community.

- Keywords
- graphs

##### Usage

```
cluster_walktrap(graph, weights = E(graph)$weight, steps = 4,
merges = TRUE, modularity = TRUE, membership = TRUE)
```

##### Arguments

- graph
The input graph, edge directions are ignored in directed graphs.

- weights
The edge weights.

- steps
The length of the random walks to perform.

- merges
Logical scalar, whether to include the merge matrix in the result.

- modularity
Logical scalar, whether to include the vector of the modularity scores in the result. If the

`membership`

argument is true, then it will be always calculated.- membership
Logical scalar, whether to calculate the membership vector for the split corresponding to the highest modularity value.

##### Details

This function is the implementation of the Walktrap community finding algorithm, see Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106

##### Value

`cluster_walktrap`

returns a `communities`

object, please see the `communities`

manual page for details.

##### References

Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106

##### See Also

See `communities`

on getting the actual membership
vector, merge matrix, modularity score, etc.

`modularity`

and `cluster_fast_greedy`

,
`cluster_spinglass`

,
`cluster_leading_eigen`

,
`cluster_edge_betweenness`

for other community detection
methods.

##### Examples

```
# NOT RUN {
g <- make_full_graph(5) %du% make_full_graph(5) %du% make_full_graph(5)
g <- add_edges(g, c(1,6, 1,11, 6, 11))
cluster_walktrap(g)
# }
```

*Documentation reproduced from package igraph, version 1.0.1, License: GPL (>= 2)*