Show quoted text>
> Since I'm interested in the Laplacian spectrum of a graph, I need to use
> the matrix as a whole, and returning individual elements of the matrix
> would be of no use to my application. The normal definition of the
> Laplacian of a graph is something like what is stated here:
>
>
http://mathworld.wolfram.com/LaplacianMatrix.html
Yes, but I remain very doubtful about whether my rather complex
(read: slow *and* big) implementation of Graphs is any good for
the linear algebra approach like the Laplacian. PDL sounds like
a much better match.
Show quoted text> However, another equivalent definition can be defined in terms of the
> Adjacency matrix.
>
> The Laplacian and adjacency matrices can be related as follows. Let D be
> a n x n diagonal matrix such that D_vv = \sum_w A_vw. We can compute
> L(G) as L = D - A and the normalized Laplacian as
> L = D^{1/2} x L x D^{1/2}. Thus the normalized Laplacian represents a
> rescaling by the number of edges per vertex.
>
> Do you think you could perform this calculation based on the
> Graph::Adjacency implementation? If this isn't clear, please let me know
> and I can give more information. I just snipped a section from my paper
> where I relate the Laplacian to the Adjacency matrix.
>
> Thanks,
> -Marc
--
Jarkko Hietaniemi <jhi@iki.fi>
http://www.iki.fi/jhi/ "There is this special
biologist word we use for 'stable'. It is 'dead'." -- Jack Cohen