membership  R Documentation 
igraph community detection functions return their results as an object from
the communities
class. This manual page describes the operations of
this class.
membership(communities)
## S3 method for class 'communities'
print(x, ...)
## S3 method for class 'communities'
modularity(x, ...)
## S3 method for class 'communities'
length(x)
sizes(communities)
algorithm(communities)
merges(communities)
crossing(communities, graph)
code_len(communities)
is_hierarchical(communities)
## S3 method for class 'communities'
as.dendrogram(object, hang = 1, use.modularity = FALSE, ...)
## S3 method for class 'communities'
as.hclust(x, hang = 1, use.modularity = FALSE, ...)
as_phylo(x, ...)
## S3 method for class 'communities'
as_phylo(x, use.modularity = FALSE, ...)
cut_at(communities, no, steps)
show_trace(communities)
## S3 method for class 'communities'
plot(
x,
y,
col = membership(x),
mark.groups = communities(x),
edge.color = c("black", "red")[crossing(x, y) + 1],
...
)
communities, x, object 
A 
... 
Additional arguments. 
graph 
An igraph graph object, corresponding to 
hang 
Numeric scalar indicating how the height of leaves should be
computed from the heights of their parents; see 
use.modularity 
Logical scalar, whether to use the modularity values to define the height of the branches. 
no 
Integer scalar, the desired number of communities. If too low or
two high, then an error message is given. Exactly one of 
steps 
The number of merge operations to perform to produce the
communities. Exactly one of 
y 
An igraph graph object, corresponding to the communities in

col 
A vector of colors, in any format that is accepted by the regular R plotting methods. This vector gives the colors of the vertices explicitly. 
mark.groups 
A list of numeric vectors. The communities can be
highlighted using colored polygons. The groups for which the polygons are
drawn are given here. The default is to use the groups given by the
communities. Supply 
edge.color 
The colors of the edges. By default the edges within communities are colored green and other edges are red. 
membership 
Numeric vector, one value for each vertex, the membership
vector of the community structure. Might also be 
algorithm 
If not 
merges 
If not 
modularity 
Numeric scalar or vector, the modularity value of the
community structure. It can also be 
Community structure detection algorithms try to find dense subgraphs in directed or undirected graphs, by optimizing some criteria, and usually using heuristics.
igraph implements a number of community detection methods (see them below),
all of which return an object of the class communities
. Because the
community structure detection algorithms are different, communities
objects do not always have the same structure. Nevertheless, they have some
common operations, these are documented here.
The print()
generic function is defined for communities
, it
prints a short summary.
The length
generic function call be called on communities
and
returns the number of communities.
The sizes()
function returns the community sizes, in the order of their
ids.
membership()
gives the division of the vertices, into communities. It
returns a numeric vector, one value for each vertex, the id of its
community. Community ids start from one. Note that some algorithms calculate
the complete (or incomplete) hierarchical structure of the communities, and
not just a single partitioning. For these algorithms typically the
membership for the highest modularity value is returned, but see also the
manual pages of the individual algorithms.
communities()
is also the name of a function, that returns a list of
communities, each identified by their vertices. The vertices will have
symbolic names if the add.vertex.names
igraph option is set, and the
graph itself was named. Otherwise numeric vertex ids are used.
modularity()
gives the modularity score of the partitioning. (See
modularity.igraph()
for details. For algorithms that do not
result a single partitioning, the highest modularity value is returned.
algorithm()
gives the name of the algorithm that was used to calculate
the community structure.
crossing()
returns a logical vector, with one value for each edge,
ordered according to the edge ids. The value is TRUE
iff the edge
connects two different communities, according to the (best) membership
vector, as returned by membership()
.
is_hierarchical()
checks whether a hierarchical algorithm was used to
find the community structure. Some functions only make sense for
hierarchical methods (e.g. merges()
, cut_at()
and
as.dendrogram()
).
merges()
returns the merge matrix for hierarchical methods. An error
message is given, if a nonhierarchical method was used to find the
community structure. You can check this by calling is_hierarchical()
on
the communities
object.
cut_at()
cuts the merge tree of a hierarchical community finding method,
at the desired place and returns a membership vector. The desired place can
be expressed as the desired number of communities or as the number of merge
steps to make. The function gives an error message, if called with a
nonhierarchical method.
as.dendrogram()
converts a hierarchical community structure to a
dendrogram
object. It only works for hierarchical methods, and gives
an error message to others. See stats::dendrogram()
for details.
as.hclust
is similar to as.dendrogram()
, but converts a
hierarchical community structure to a hclust
object.
as_phylo()
converts a hierarchical community structure to a phylo
object, you will need the ape
package for this.
show_trace()
works (currently) only for communities found by the leading
eigenvector method (cluster_leading_eigen()
), and
returns a character vector that gives the steps performed by the algorithm
while finding the communities.
code_len()
is defined for the InfoMAP method
(cluster_infomap()
and returns the code length of the
partition.
It is possibly to call the plot()
function on communities
objects. This will plot the graph (and uses plot.igraph()
internally), with the communities shown. By default it colores the vertices
according to their communities, and also marks the vertex groups
corresponding to the communities. It passes additional arguments to
plot.igraph()
, please see that and also
igraph.plotting on how to change the plot.
print()
returns the communities
object itself,
invisibly.
length
returns an integer scalar.
sizes()
returns a numeric vector.
membership()
returns a numeric vector, one number for each vertex in
the graph that was the input of the community detection.
modularity()
returns a numeric scalar.
algorithm()
returns a character scalar.
crossing()
returns a logical vector.
is_hierarchical()
returns a logical scalar.
merges()
returns a twocolumn numeric matrix.
cut_at()
returns a numeric vector, the membership vector of the
vertices.
as.dendrogram()
returns a dendrogram object.
show_trace()
returns a character vector.
code_len()
returns a numeric scalar for communities found with the
InfoMAP method and NULL
for other methods.
plot()
for communities
objects returns NULL
, invisibly.
#' @author Gabor Csardi csardi.gabor@gmail.com
See plot_dendrogram()
for plotting community structure
dendrograms.
See compare()
for comparing two community structures
on the same graph.
The different methods for finding communities, they all return a
communities
object: cluster_edge_betweenness()
,
cluster_fast_greedy()
,
cluster_label_prop()
,
cluster_leading_eigen()
,
cluster_louvain()
, cluster_leiden()
,
cluster_optimal()
, cluster_spinglass()
,
cluster_walktrap()
.
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
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()
karate < make_graph("Zachary")
wc < cluster_walktrap(karate)
modularity(wc)
membership(wc)
plot(wc, karate)
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