As a true R fan, I like to believe that R can do anything, no matter how big, how small or how complicated: there is some way to do it in R. I decided to approach my large, sparse matrix problem with this attitude. But here I sit a broken man.
There is no “native” big data support built into R, even if using the 64bit build of R. Before venturing on this endeavor, I consulted with my advisor who reassured me that R uses the state of the art for sparse matrices. That was enough for me.
For part of my Masters thesis, I wrote code to extract all of the friends and followers out to network degree 2 to construct a “small-world” snapshot of a user via their relationships. In a graph, nodes and edges grow exponentially as the degree increases. The number of nodes was on the order of 300,000. The number of edges I predict will be around 900,000. The code is still running. This means that a dense matrix would have size . Some of you already know how this story is going to end…
The matrix is very sparse.
The raw data graph.log consists of an […]