igraph (version 0.6.5-2)

fastgreedy.community: Community structure via greedy optimization of modularity

Description

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

Usage

fastgreedy.community(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 edge

Value

• fastgreedy.community returns a communities object, please see the communities manual page for details.

concept

• Community structure
• Fast greedy community detection

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.

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 walktrap.community, spinglass.community, leading.eigenvector.community and edge.betweenness.community for other methods.

Examples

g <- graph.full(5) %du% graph.full(5) %du% graph.full(5)
g <- add.edges(g, c(1,6, 1,11, 6, 11))
fc <- fastgreedy.community(g)
membership(fc)
sizes(fc)

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