# cluster_fast_greedy

##### Community structure via greedy optimization of modularity

This function tries to find dense subgraph, also called communities in graphs via directly optimizing a modularity score.

- Keywords
- graphs

##### Usage

```
cluster_fast_greedy(graph, merges = TRUE, modularity = TRUE,
membership = TRUE, weights = E(graph)$weight)
```

##### Arguments

- graph
The input graph

- merges
Logical scalar, whether to return the merge matrix.

- modularity
Logical scalar, whether to return a vector containing the modularity after each merge.

- membership
Logical scalar, whether to calculate the membership vector corresponding to the maximum modularity score, considering all possible community structures along the merges.

- 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.

##### Details

This function implements the fast greedy modularity optimization algorithm for finding community structure, see A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/0408187 for the details.

##### Value

`cluster_fast_greedy`

returns a `communities`

object, please see the `communities`

manual page for details.

##### References

A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/0408187

##### See Also

`communities`

for extracting the results.

See also `cluster_walktrap`

,
`cluster_spinglass`

,
`cluster_leading_eigen`

and
`cluster_edge_betweenness`

for other 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))
fc <- cluster_fast_greedy(g)
membership(fc)
sizes(fc)
# }
```

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