an instance of the graph class with edgemode
undirected
threshold
threshold to terminate clustering process
normalize
boolean, when TRUE, the edge betweenness centrality is
scaled by 2/((n-1)(n-2)) where n is the number of vertices
in g; when FALSE, the edge betweenness centrality is the absolute
value
Value
A list of
no.of.edges
number of remaining edges after removal
edges
remaining edges
edge.betweenness.centrality
betweenness centrality of remaining edges
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.
The Boost Graph Library: User Guide and Reference Manual;
by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine;
(Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp.
ISBN 0-201-72914-8