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 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

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

See Also

brandes.betweenness.centrality

Aliases
  • betweenness.centrality.clustering
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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