Title: Fast Unbiased Sampling of Networks with Given Expected Degrees and Strengths Show Chinese title

Title: 快速无偏网络采样具有给定期望度和强度

Abstract: The configuration model is a cornerstone of statistical assessment of network structure. While the Chung-Lu model is among the most widely used configuration models, it systematically oversamples edges between large-degree nodes, leading to inaccurate statistical conclusions. Although the maximum entropy principle offers unbiased configuration models, its high computational cost has hindered widespread adoption, making the Chung-Lu model an inaccurate yet persistently practical choice. Here, we propose fast and efficient sampling algorithms for the max-entropy-based models by adapting the Miller-Hagberg algorithm. Evaluation on 103 empirical networks demonstrates 10-1000 times speedup, making theoretically rigorous configuration models practical and contributing to a more accurate understanding of network structure. Show Chinese abstract

Abstract: 配置模型是统计评估网络结构的基石。 尽管 Chung-Lu 模型是最常用的配置模型之一，但它会系统性地过度采样大度数节点之间的边，导致统计结论不准确。 虽然最大熵原理提供了无偏的配置模型，但其高昂的计算成本阻碍了广泛应用，使得 Chung-Lu 模型成为一个不准确但持续实用的选择。 在这里，我们通过适应 Miller-Hagberg 算法，提出了基于最大熵的模型的快速高效采样算法。 在 103 个经验网络上的评估表明，速度提高了 10 到 1000 倍，使理论严谨的配置模型变得实用，并有助于更准确地理解网络结构。