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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021
摘要
The configuration model is a standard tool for 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 struc- ture is explained by its degrees alone. Except for 網絡s with both self-loops and multi-edges, we lack a direct sampling algorithm for the configuration model, e.g., for simple graphs. 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 without bias. However, accurately de-tecting convergence of this 馬可夫 chain on its stationary distribution remains an unsolved problem. Here, we provide a concrete solution to detect convergence and sample from the configuration model without bias. We first develop an algorithm for est
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