{"ID":2870527,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13230","arxiv_id":"2509.13230","title":"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.","short_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 offe...","url_abs":"https://arxiv.org/abs/2509.13230","url_pdf":"https://arxiv.org/pdf/2509.13230v4","authors":"[\"Xuanchi Li\",\"Xin Wang\",\"Sadamori Kojaku\"]","published":"2025-09-16T16:38:23Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"physics.soc-ph\"]","methods":"[]","has_code":false}