Community structure via greedy optimization of modularity
Source:R/community.R
cluster_fast_greedy.Rd
This function tries to find dense subgraph, also called communities in graphs via directly optimizing a modularity score.
Usage
cluster_fast_greedy(
graph,
merges = TRUE,
modularity = TRUE,
membership = TRUE,
weights = NULL
)
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
The weights of the edges. It must be a positive numeric vector,
NULL
orNA
. If it isNULL
and the input graph has a ‘weight’ edge attribute, then that attribute will be used. IfNULL
and no such attribute is present, then the edges will have equal weights. Set this toNA
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.
Value
cluster_fast_greedy()
returns a communities()
object, please see the communities()
manual page for details.
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 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()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
Author
Tamas Nepusz ntamas@gmail.com and Gabor Csardi csardi.gabor@gmail.com for the R interface.
Examples
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)
#> [1] 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