Community structure via greedy optimization of modularitySource:
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 )
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,
NA. If it is
NULLand the input graph has a ‘weight’ edge attribute, then that attribute will be used. If
NULLand no such attribute is present, then the edges will have equal weights. Set this to
NAif 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.
cluster_fast_greedy() returns a
object, please see the
communities() manual page for 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.
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.
cluster_leiden() for other methods.
Tamas Nepusz firstname.lastname@example.org and Gabor Csardi email@example.com for the R interface.
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) #>  3 3 3 3 3 1 1 1 1 1 2 2 2 2 2 sizes(fc) #> Community sizes #> 1 2 3 #> 5 5 5