cluster_fast_greedy  R Documentation 
This function tries to find dense subgraph, also called communities in graphs via directly optimizing a modularity score.
cluster_fast_greedy(
graph,
merges = TRUE,
modularity = TRUE,
membership = TRUE,
weights = NULL
)
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 
The weights of the edges. It must be a positive numeric vector,

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/condmat/0408187 for the details.
cluster_fast_greedy()
returns a communities()
object, please see the communities()
manual page for details.
Tamas Nepusz ntamas@gmail.com and Gabor Csardi csardi.gabor@gmail.com for the R interface.
A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/condmat/0408187
communities()
for extracting the results.
See also cluster_walktrap()
,
cluster_spinglass()
,
cluster_leading_eigen()
and
cluster_edge_betweenness()
, cluster_louvain()
cluster_leiden()
for other methods.
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fluid_communities()
,
cluster_infomap()
,
cluster_label_prop()
,
cluster_leading_eigen()
,
cluster_leiden()
,
cluster_louvain()
,
cluster_optimal()
,
cluster_spinglass()
,
cluster_walktrap()
,
compare()
,
groups()
,
make_clusters()
,
membership()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
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)
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