The paper unifies sequential random graph generation as sampling maximal independent sets, classifies the only two infinite families that allow asymptotic uniformity, and proves error bounds for maximum degree up to m^{1/4}/log m.
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Unified framework for asymptotically uniform iterative construction of generalised random graphs with local constraints
The paper unifies sequential random graph generation as sampling maximal independent sets, classifies the only two infinite families that allow asymptotic uniformity, and proves error bounds for maximum degree up to m^{1/4}/log m.