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 <

[hidden email]> 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 <

[hidden email]> 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 <

[hidden email]> wrote:

>>

>>> To clarify: I don't need the actual paths, just the distances.

>>>

>>> On Wed, Mar 26, 2014 at 3:04 PM, Ryan Compton <

[hidden email]> wrote:

>>>> No idea how feasible this is. Has anyone done it?

>>