# cluster_fast_greedy

From igraph v1.0.0
by Gabor Csardi

##### 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 edge attribute is used as weights. If it is not present, then all edges are consi`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

```
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 1.0.0, License: GPL (>= 2)*

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