On 13.02.2017 17:05, Snehal Shekatkar wrote:

Hello Tiago,

Thanks for the reply. I have included all the commands in the previous email. My degree_sampler is a function:

def deg_samp(k0 = 3): return np.random.poisson(k0)

Then I use it in two different (and in my opinion, equivalent) ways:

G = gt.random_graph(500, lambda : deg_samp(3), directed = False) G = gt.random_graph(500, deg_samp, directed = False)

I expect both these graphs to be similar. However, I find that the second one is extremely dense and its average degree far exceeds 3. I am quite confused about this.

The function random_graph() will look at how many parameters the deg_sampler takes, and this will trigger different behaviors. From the documentation: A degree sampler function which is called without arguments, and returns a tuple of ints representing the in and out-degree of a given vertex (or a single int for undirected graphs, representing the out-degree). This function is called once per vertex, but may be called more times, if the degree sequence cannot be used to build a graph. Optionally, you can also pass a function which receives one or two arguments. If block_membership is None, the single argument passed will be the index of the vertex which will receive the degree. If block_membership is not None, the first value passed will be the vertex index, and the second will be the block value of the vertex. Your first example will trigger the behavior in the first paragraph, while the second will trigger the behavior in the second paragraph. Although it is in fact documented, I agree this is confusing and unexpected. Please open an issue in the website, and this will be improved in the future. Best, Tiago -- Tiago de Paula Peixoto <tiago@skewed.de>