Use this if you are using igraph from R
cluster_edge_betweenness {igraph}  R Documentation 
Many networks consist of modules which are densely connected themselves but sparsely connected to other modules.
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 edge betweenness score of an edge measures the number of shortest paths
through it, see edge_betweenness
for details. The idea of the
edge betweenness based community structure detection is that it is likely
that edges connecting separate modules have high edge betweenness as all the
shortest paths from one module to another must traverse through them. So if
we gradually remove the edge with the highest edge betweenness score we will
get a hierarchical map, a rooted tree, called a dendrogram of the graph. The
leafs of the tree are the individual vertices and the root of the tree
represents the whole graph.
cluster_edge_betweenness
performs this algorithm by calculating the
edge betweenness of the graph, removing the edge with the highest edge
betweenness score, then recalculating edge betweenness of the edges and
again removing the one with the highest score, etc.
edge.betweeness.community
returns various information collected
through the run of the algorithm. See the return value down here.
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.
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