On Sun, Mar 9, 2008 at 9:38 PM, Tamas Nepusz <
address@hidden> wrote:
Hi,
> I need to compute some extremal eigenvalues/eigenvectors of
> adjacency and Laplacian matrices for very large and sparse graphs.
> (to large for the matrices to be represented in their dense form
> anyway).
>
> Given the graphs, how can I achieve this task with igraph?
igraph includes an interface to the ARPACK library suitable for
solving large scale eigenvalue problems. See the following page in the
documentation for details:
http://cneurocvs.rmki.kfki.hu/igraph/doc/html/ch19s01.html
So you should import your graph somehow into igraph (using the C core
directly), initialize an igraph_arpack_options_t data structure, set
its options appropriately for your specific problem, implement an
igraph_arpack_function_t function that multiplies a given vector with
your matrix (chances are that you'll have to use an adjacency list
representation of your graph, see http://cneurocvs.rmki.kfki.hu/igraph/doc/html/igraph-Adjlists.html)
, and finally call igraph_arpack_rssolve if your matrix is symmetric,
igraph_arpack_rnsolve otherwise.
If you need examples, check the code for PageRank calculation or
eigenvector centrality in centrality.c in the igraph source code.
I hope this helps.
Regards,
--
T.
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