Graph clustering based on edge betweenness centrality
Graph clustering based on edge betweenness centrality
betweenness.centrality.clustering(g, threshold = -1, normalize = TRUE)
g |
an instance of the |
threshold |
threshold to terminate clustering process |
normalize |
boolean, when TRUE, the edge betweenness centrality is
scaled by |
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.
A list of
no.of.edges |
number of remaining edges after removal |
edges |
remaining edges |
edge.betweenness.centrality |
betweenness centrality of remaining edges |
Li Long <li.long@isb-sib.ch>
Boost Graph Library ( www.boost.org/libs/graph/doc/index.html )
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
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