R: Add weighted edge.betweenness.community

[Andrew Edelman]

Would allow edge weighting to be used in edge betweenness method of community detection. For algorithm see Newman, M.E.J. 2004. Analysis of weighted networks. Physical Review E. vol. 70 (2), no5, pp. 056131.1-056131.9.

[Gábor Csárdi (gabor.csardi)]

Since we already have weighted edge-betweenness calculation, it should not be too hard. The only problem is that edge.betweenness community does not call edge.betweenness, but does the calculation itself, because of performance reasons.

But it could be easily rewritten to call an internal version of edge.betweenness.community, that does not require the reallocation of memory all the time.

[Andrew Edelman (aedelman)]

  Thanks for your response, Gábor. It would be great to have this
feature added, since most of the other community detection functions can
handle weighted networks. I also noticed that the
leading.eigenvector.community function doesn't use weights. I haven't
been able to find a detailed reference about how weights could be
accommodated for the leading eigenvector method. However, David Lusseau,
who has work with M. Newman, briefly outlines a weighted version (Animal
Behaviour, 2008, vol 75, pg 1812). There might be enough info in that
reference to figure out how to add this feature too. Thanks for all your
work to incorporate weighted network analysis into igraph.
Best,

Andrew

Andrew Edelman, Ph.D.
NSF Postdoctoral Fellow in Bioinformatics
Zoology& Physiology, Dept. 3166
1000 E. University Ave.
University of Wyoming
Laramie, WY 82071
Phone: [phone]
Email: <email address hidden>
www.unm.edu/~andrewe

On 9/16/2010 3:21 AM, Gábor Csárdi wrote:
> Since we already have weighted edge-betweenness calculation, it should
> not be too hard. The only problem is that edge.betweenness community
> does not call edge.betweenness, but does the calculation itself, because
> of performance reasons.
>
> But it could be easily rewritten to call an internal version of
> edge.betweenness.community, that does not require the reallocation of
> memory all the time.
>
>
> ** Changed in: igraph
> Status: New => Confirmed
>
> ** Changed in: igraph
> Importance: Undecided => Wishlist
>
> ** Changed in: igraph
> Assignee: (unassigned) => Gábor Csárdi (gabor.csardi)
>

[Tamás Nepusz (ntamas)]

Added in #2692 in trunk. R interface requires further adjustments because the weights parameter is not used yet. The Python interface has been updated.

[Gábor Csárdi (gabor.csardi)]

The development of igraph has moved to github, so please do not comment on this bug here. You are of course welcome to comment on github, here:
https://github.com/igraph/igraph/issues/337