Arash Badie-Modiri

You can also chain a generator of np.randoms of a few millions or so to potentially get the best of both worlds.

@maximelucas The combinatorial explosion of the mask array makes it impossible to use in some scenarios: e.g. ```pycon >>> import xgi >>> xgi.random_hypergraph(1000, order=5, ps=[1e-12]) # => should create a...

@nwlandry That makes total sense. My current WIP implementation is also [based on Cartesian product](https://github.com/reticula-network/reticula/blob/42ed11ebd3cccb16af2ad66dd803ba07d9940cb2/src/random_networks/uniform_hypergraph.tpp#L53-L58) as this help alleviate the issue with fixed sized integers, but I have to [reject](https://github.com/reticula-network/reticula/blob/42ed11ebd3cccb16af2ad66dd803ba07d9940cb2/src/random_networks/uniform_hypergraph.tpp#L63-L64)...

I think it's a problem of naming and expectations. In dyadic undirected Erdős-Rényi $G(n, p)$ networks, edges $(i, j)$ and $(j, i)$ don't get a separate Bernoulli trial each with...

I'd be glad to participate in development of this gem. I just started to use this gem to correct user input data when rendering view. It would not be a...

I think there is a workaround for this issue. simply by caching those gigantic and most-wanted use-input data in _another_ field or something like this so the process is only...

+1

Currently the last places that use hard-coded uniform random bit generator are the exponential and geometric temporal adjacency types.

Hi Martín! This sounds great. I'll try to remember to add this in the next round of development. The main issue is that it will be highly beneficial to add...

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