Interesting... There is a much more recent work by their group [Faloutsos et al.] on mining Peta scale graphs using Hadoop, they have released their code called as Pegasus under Apache License.
http://www.cs.cmu.edu/~pegasus/ I am currently using their code, and It would be a worthwhile addition to the Mahout. I remember reading presentation by someone, (I guess it was by Jake Mannix) about how matrix multiplication is basis for most of the operations. Their group seems to have developed a block based algorithm to speed up multiplication of a Sparse Matrix with a dense column vector and using this multiplication as a primitive they have implemented numerous algorithms optimized for use on hadoop/map-reduce. On Fri, Oct 1, 2010 at 4:55 PM, Ted Dunning <[email protected]> wrote: > Jake, > > You asked a bit ago about strategies for very large SVD's. > > I wonder if interpolative decompositions might be an avenue toward that. > > See, for instance, Less is More: Compact Matrix Decomposition for Large > Sparse Graphs <http://www.cs.cmu.edu/~jimeng/papers/SunSDM07.pdf> > > The idea is that if your basis vectors are sparse, you might do much better > in terms of space. > -- Akshay Uday Bhat. Graduate Student, Computer Science, Cornell University Website: http://www.akshaybhat.com
