To follow up on your answer in case others will encounter it someday: Using random_graph is indeed much faster (0.01s as opposed to 7s for a fully connected graph with 500 vertices)

An important pitfall when using random_graph to generate a graph is that the edges of the graph aren't iterated over in the order of their index. This means that assigning values to the underlying array of a propertyMap is tricky. A simple fix is to call

graph.reindex_edges()

after generating the graph. This operation also runs in about 0.01s on a fully connected graph of 500 nodes on my machine.

On 19 March 2013 21:35, Jonas Arnfred <jonas@ifany.org> wrote:

Thanks a lot, I'll try it out!

On 19 March 2013 18:51, Tiago de Paula Peixoto <tiago@skewed.de> wrote:

Hi,

On 03/19/2013 09:39 AM, arnfred wrote:
> I'm currently trying to instantiate a fully connected graph with some 600
> vertices, but I find that adding all the edges usually takes around 10
> seconds on my system. The fastest way of doing it that I have come up with
> so far is to write:
>
> from itertools import combinations
> edges = [g.add_edge(v1,v2) for (v1,v2) in combinations(g.vertices(),2)]
>
> But I'm wondering if there is a faster method?

You can create a "random" graph with all degrees equal to N - 1:

g = random_graph(600, lambda: 600 - 1, directed=False, random=False)

This should be much faster. Note the option 'random=False' which avoids
the random placement of the edges, which would be completely pointless
in this case.

I'm planning to include a complete graph generator, as well as some
other simple generators, which would make this more straightforward.

Cheers,
Tiago

--
Tiago de Paula Peixoto <tiago@skewed.de>

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