betweenness.centrality.clustering

From RBGL v1.48.1 by Bioconductor Package Maintainer

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 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

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

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, Lie-Quan Lee, and Andrew Lumsdaine; (Addison-Wesley, Pearson Education Inc., 2002), xxiv+321pp. ISBN 0-201-72914-8

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)

Documentation reproduced from package RBGL, version 1.48.1, License: Artistic-2.0

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