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Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees

This document introduces a sequential importance sampling algorithm designed for generating random graphs that adhere to specified degree distributions, exemplified by the case where degrees are given as d = (2, 2, 2). It compares this algorithm with several others, including random graph algorithms with predefined degree distributions, the pairing model, variations of adjacency lists and the Havel-Hakimi algorithm, as well as Markov Chain Monte Carlo techniques (MCMC). The focus is on the efficiency and effectiveness of the sequential algorithm in graph generation.

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Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees

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  1. A SEQUENTIAL IMPORTANCE SAMPLING ALGORITHM FORGENERATING RANDOM GRAPHS WITH PRESCRIBED DEGREES (Introduction)

  2. For example d = (2, 2, 2)

  3. Other Algorithms 1. Algorithms for random graphs with given degree distributions. 2. The Pairing Model. 3. Algorithms Based on the Pairing Model 4. Adjacency Lists and Havel-Hakimi Variants. 5. Markov Chain Monte Carlo Algorithms(MCMC).

  4. The Sequential Algorithm

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