cluster_fast_greedy

0th

Percentile

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. Larger edge weights correspond to stronger connections.

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.

Aliases
  • cluster_fast_greedy
  • fastgreedy.community
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 1.2.2, License: GPL (>= 2)

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