betweenness.centrality.clustering
Graph clustering based on edge betweenness centrality
Graph clustering based on edge betweenness centrality
 Keywords
 models
Usage
betweenness.centrality.clustering(g, threshold = 1, normalize = T)
Arguments
 g
 an instance of the
graph
class withedgemode
“undirected”  threshold
 threshold to terminate clustering process
 normalize
 boolean, when TRUE, the edge betweenness centrality is
scaled by
2/((n1)(n2))
wheren
is the number of vertices ing
; when FALSE, the edge betweenness centrality is the absolute value
Details
To implement graph clustering based on edge betweenness centrality.
The algorithm is iterative, at each step it computes the edge betweenness
centrality and removes the edge with maximum betweenness centrality when it
is above the given threshold
. When the maximum betweenness centrality
falls below the threshold, the algorithm terminates.
See documentation on Clustering algorithms in Boost Graph Library for details.
Value

A list of
 no.of.edges
 number of remaining edges after removal
 edges
 remaining edges
 edge.betweenness.centrality
 betweenness centrality of remaining edges
References
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
The Boost Graph Library: User Guide and Reference Manual; by Jeremy G. Siek, LieQuan Lee, and Andrew Lumsdaine; (AddisonWesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0201729148
See Also
Examples
con < file(system.file("XML/conn.gxl",package="RBGL"))
coex < fromGXL(con)
close(con)
coex < ugraph(coex)
betweenness.centrality.clustering(coex, 0.5, TRUE)
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