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
)cluster_fast_greedy returns a communities
object, please see the communities manual page for details.
The input graph
Logical scalar, whether to return the merge matrix.
Logical scalar, whether to return a vector containing the modularity after each merge.
Logical scalar, whether to calculate the membership vector corresponding to the maximum modularity score, considering all possible community structures along the merges.
The weights of the edges. It must be a positive numeric vector,
NULL or NA. If it is NULL and the input graph has a
‘weight’ edge attribute, then that attribute will be used. If
NULL and no such attribute is present, then the edges will have equal
weights. Set this to NA if the graph was a ‘weight’ edge
attribute, but you don't want to use it for community detection. A larger
edge weight means a stronger connection for this function.
Tamas Nepusz ntamas@gmail.com and Gabor Csardi csardi.gabor@gmail.com for the R interface.
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.
A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/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.
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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