A Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees

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Directed Random Graphs with given Degree Distributions (2012, methodology): “…to the drawing of simple graphs (directed or undirected) is the development of eﬃcient simulation algorithms, see for example the recent work in [5] using importance sampling techniques for drawing a simple graph with prescribed degree sequence { d i } ; similar ideas should also be applicable…”
The sample size required in importance sampling (2015, background): “Recently, a variety of exponential random graph models have been explicitly ‘solved’ (see Chatterjee and Diaconis [16], Kenyon, Radin, Ren and Sadun [35], Kenyon and Yin [36] and Bhattacharya, Ganguly, Lubetzky and Zhao [6]).”
The statistical physics of real-world networks (2018, background): “[165] J.”
A Survey of Statistical Network Models (2009, methodology): “Software is available to sample from the space of random graphs with a given degree distribution based on Monte Carlo Markov chain methods [42; 138].”
Random graphs with a given degree sequence (2010, methodology): “See also the related work of Diaconis and Janson [19] and Austin [2] which traces this back to work of Aldous [1] and Hoover [28].”