Uniform generation of random regular graphs
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Abstract (arXiv)
We develop a new approach for uniform generation of combinatorial objects, and apply it to derive a uniform sampler REG for d-regular graphs. REG can be implemented such that each graph is generated in expected time O(nd^3), provided that d=o(n^{1/2}). Our result significantly improves the previously best uniform sampler, which works efficiently only when d=O(n^{1/3}), with essentially the same running time for the same d. We also give a linear-time approximate sampler REG*, which generates a random d-regular graph whose distribution differs from the uniform by o(1) in total variation distance, when d=o(n^{1/2}).
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Nov 2015.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023Lifted Algorithms for Symmetric Weighted First-Order Model Samplingcited by 6
- 2023On Exact Sampling in the Two-Variable Fragment of First-Order Logiccited by 3
- 2024Energy-efficient Decentralized Learning via Graph Sparsificationcited by 1
- 2026A Fast Model Counting Algorithm for Two-Variable Logic with Counting and Modulo Counting Quantifierscited by 0
- 2026A Fast Model Counting Algorithm for Two-Variable Logic with Counting and Modulo Counting Quantifiers5 May 2026
- 2024Energy-efficient Decentralized Learning via Graph Sparsification5 Jan 2024
- 2023Lifted Algorithms for Symmetric Weighted First-Order Model Sampling17 Aug 2023
- 2023On Exact Sampling in the Two-Variable Fragment of First-Order Logic6 Feb 2023