bccluster: Graph clustering based on edge betweenness centrality
In RBGL: An interface to the BOOST graph library

Description Usage Arguments Details Value Author(s) References See Also

Graph clustering based on edge betweenness centrality

1	betweenness.centrality.clustering(g, threshold = -1, normalize = TRUE)

`g`	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

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

brandes.betweenness.centrality

RBGL documentation built on Nov. 8, 2020, 5 p.m.