Hi Tiago, Sorry for bothering again. A small query: If the graph size is say 10^4, and the degrees are drawn from the discrete-power law or some other right-skewed distribution for which the second moment diverges, would n_iter = 1000 be enough for the Markov chain to saturate? Is there a rule of thumb for choosing n_iter when the scaling index of the power-law and the graph size are given? Thanks and regards, SS ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ On Wednesday, July 22, 2020 2:22 PM, Snehal Shekatkar <snehalshekatkar@protonmail.com> wrote:
Thanks so much Tiago, I highly appreciate the help. I think it would be useful to put one such reference along with others in the random_rewire docs. Please consider this if it makes sense.
Thanks and regards, SS
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‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ On Wednesday, July 22, 2020 12:47 PM, Tiago de Paula Peixoto tiago@skewed.de wrote:
Am 22.07.20 um 08:49 schrieb Snehal Shekatkar:
Thanks Tiago. I have a related question: suppose self-loops and multi-edges are not allowed. Now according to the documentation, graphs are generated using "Efficient Markov Chain based on edge swaps". However, I could not find the description of the algorithm in the documentation or the references therein. I have gone through the Karrer-Newman paper as well as your paper "Entropy of stochastic blockmodel ensembles", and both do not describe the algorithm about any rewiring using Markov chains. Could you kindly point me to the actual algorithm?
This is the usual edge-swapping algorithm that has been discovered and re-discovered many times since the 50s. You can find a good description in the recent paper: https://arxiv.org/abs/1608.00607 Best, Tiago
Tiago de Paula Peixoto tiago@skewed.de graph-tool mailing list graph-tool@skewed.de https://lists.skewed.de/mailman/listinfo/graph-tool