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Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities
[article]

2017
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arXiv
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pre-print

Common models for random graphs, such as Erdős-Rényi and Kronecker graphs, correspond to generating random adjacency matrices where each entry is non-zero based on a large matrix of probabilities. Generating an instance of a random graph based on these models is easy, although inefficient, by flipping biased coins (i.e. sampling binomial random variables) for each possible edge. This process is inefficient because most large graph models correspond to sparse graphs where the vast majority of

arXiv:1709.03438v1
fatcat:rqweozesfnaejjyhnbidjvdkre