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Huge thanks to all that replied. Here's a quick summary for the archive:
There are several levels of solution, depending on the how big your
network is. First,
Jacon Mumm reminds to set R's memory allocation: "R 2.5.1--max-
Tore Opsahl adds the help for this: http://stat.ethz.ch/R-manual/R-
If that doesn't help, maybe storing the data in a more efficient way
Mark Handcock points out (with empirical proof!) that R's network
package does handle such networks (albeit not all of its functions),
if they are stored in the right format.
Mason Porter offers "NetworkX" as a good tool for changing data formats.
Gabriel Ripoche suggests moving to the igraph package, which uses
sparse data on all functions.
Other alternatives requires real programming skills:
Robert Ackland suggests moving to the boost graph library (http://
www.boost.org/libs/graph/doc/index.html) - Which requires writing
your own SNA functions in C++
Adam M. Kleinbaum is working with Csparse (http://www.cise.ufl.edu/
research/sparse/CSparse/) Which is very efficient, lacking network
functions, and is in C
Joshua O'Madadhain tells about JUNG - jung.sourceforge.net - Which
actually have some SNA functions, and is in Java.
Finally, Mark Supekar suggests a software solution that is free for a
15 day trial...available at http://www.atalab.com/software/dynet/
download.php, manual at http://www.atalab.com/software/dynet/manual/
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