Sorry for joining this discussion late. If you are only interested in the four largest eigenvalues, there are more efficient algorithms out there than just eig(). There are algorithms that just give you the N largest. Then again, I don't know of any Python implementations, but I haven't looked, Mark
On Apr 29, 11:04 pm, "Matthieu Brucher" matthieu.bruc...@gmail.com wrote:
2007/4/29, Anton Sherwood anton.sherw...@gmail.com:
Anton Sherwood wrote:
I'm using eigenvectors of a graph's adjacency matrix as "topological" coordinates of the graph's vertices as embedded in 3space (something I learned about just recently). Whenever I've done this with a graph
*does* have a good 3d embedding, using the first eigenvector results
a flat model: apparently the first is not independent, at least in
cases. . . .
Charles R Harris wrote:
. . . the embedding part sounds interesting, I'll have to think about why that works.
It's a mystery to me: I never did study enough matrix algebra to get a feel for eigenvectors (indeed this is the first time I've had anything to do with them).
I'll happily share my code with anyone who wants to experiment with it.
Seems to me that this is much like Isomap and class multidimensional scaling, no ?
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There are several subroutines in LAPACK for this task.
IIRC symeig provides a wrapper. See