cluster_edge_betweenness  R Documentation 
Community structure detection based on the betweenness of the edges in the network. This method is also known as the GirvanNewman algorithm.
cluster_edge_betweenness(
graph,
weights = NULL,
directed = TRUE,
edge.betweenness = TRUE,
merges = TRUE,
bridges = TRUE,
modularity = TRUE,
membership = TRUE
)
graph 
The graph to analyze. 
weights 
The weights of the edges. It must be a positive numeric vector,

directed 
Logical constant, whether to calculate directed edge betweenness for directed graphs. It is ignored for undirected graphs. 
edge.betweenness 
Logical constant, whether to return the edge betweenness of the edges at the time of their removal. 
merges 
Logical constant, whether to return the merge matrix
representing the hierarchical community structure of the network. This
argument is called 
bridges 
Logical constant, whether to return a list the edge removals which actually splitted a component of the graph. 
modularity 
Logical constant, whether to calculate the maximum modularity score, considering all possibly community structures along the edgebetweenness based edge removals. 
membership 
Logical constant, whether to calculate the membership vector corresponding to the highest possible modularity score. 
The idea behind this method is that the betweenness of the edges connecting two communities is typically high, as many of the shortest paths between vertices in separate communities pass through them. The algorithm successively removes edges with the highest betweenness, recalculating betweenness values after each removal. This way eventually the network splits into two components, then one of these components splits again, and so on, until all edges are removed. The resulting hierarhical partitioning of the vertices can be encoded as a dendrogram.
cluster_edge_betweenness()
returns various information collected
through the run of the algorithm. Specifically, removed.edges
contains
the edge IDs in order of the edges' removal; edge.betweenness
contains
the betweenness of each of these at the time of their removal; and
bridges
contains the IDs of edges whose removal caused a split.
cluster_edge_betweenness()
returns a
communities()
object, please see the communities()
manual page for details.
Gabor Csardi csardi.gabor@gmail.com
M Newman and M Girvan: Finding and evaluating community structure in networks, Physical Review E 69, 026113 (2004)
edge_betweenness()
for the definition and calculation
of the edge betweenness, cluster_walktrap()
,
cluster_fast_greedy()
,
cluster_leading_eigen()
for other community detection
methods.
See communities()
for extracting the results of the community
detection.
Community detection
as_membership()
,
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()
,
membership()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
,
voronoi_cells()
g < sample_pa(100, m = 2, directed = FALSE)
eb < cluster_edge_betweenness(g)
g < make_full_graph(10) %du% make_full_graph(10)
g < add_edges(g, c(1, 11))
eb < cluster_edge_betweenness(g)
eb
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