Fast Unbiased Sampling of Networks with Given Expected Degrees and Strengths
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.
Cite
@article{arxiv.2509.13230,
title = {Fast Unbiased Sampling of Networks with Given Expected Degrees and Strengths},
author = {Xuanchi Li and Xin Wang and Sadamori Kojaku},
journal= {arXiv preprint arXiv:2509.13230},
year = {2025}
}