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原文連結
論文資訊
- 類型：已發表論文
- 日期：2024-10-24
摘要
The configuration model is a standard tool for uniformly generating 隨機圖s with a specified degree sequence, and is often used as a null model to evaluate how much of an observed 網絡's structure can be explained by its degree structure alone. A 馬可夫 chain 蒙地卡羅 (MCMC) algorithm, based on a degree-preserving double-edge swap, provides an asymptotic solution to sample from the configuration model. However, accurately and efficiently detecting when this 馬可夫 chain is sufficiently close to its stationary distribution remains an unsolved problem. Here, we provide a solution to sample from the configuration model using this standard MCMC algorithm. We develop an algorithm, based on the assortativity of the sampled graphs, for estimating the gap between effectively independent MCMC states, and a 計算ly e
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