Yeah, if you’re just worried about statistics, maybe you can do sampling (do
single-pair paths from 100 random nodes and you get an idea of what percentage
of nodes have what distribution of neighbors in a given distance).
Matei
On Mar 26, 2014, at 5:55 PM, Ryan Compton compton.r...@gmail.com wrote:
Much thanks, I suspected this would be difficult. I was hoping to
generate some 4 degrees of separation-like statistics. Looks like
I'll just have to work with a subset of my graph.
On Wed, Mar 26, 2014 at 5:20 PM, Matei Zaharia matei.zaha...@gmail.com
wrote:
All-pairs distances is tricky for a large graph because you need O(V^2)
storage. Do you want to just quickly query the distance between two
vertices? In that case you can do single-source shortest paths, which I
believe exists in GraphX, or at least is very quick to implement on top of
its Pregel API. If your graph is small enough that storing all-pairs is
feasible, you can probably run this as an iterative algorithm:
http://en.wikipedia.org/wiki/Floyd–Warshall_algorithm, though I haven’t
tried it. It may be tough to do with GraphX.
Matei
On Mar 26, 2014, at 3:51 PM, Ryan Compton compton.r...@gmail.com wrote:
To clarify: I don't need the actual paths, just the distances.
On Wed, Mar 26, 2014 at 3:04 PM, Ryan Compton compton.r...@gmail.com
wrote:
No idea how feasible this is. Has anyone done it?
