Fast Generation of Random Spanning Trees and the Effective Resistance Metric
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Abstract (arXiv)
We present a new algorithm for generating a uniformly random spanning tree in an undirected graph. Our algorithm samples such a tree in expected tildeO(m4/3) time. This improves over the best previously known bound of min(tildeO(m√n),O(nω)) -- that follows from the work of Kelner and Mkadry [FOCS'09] and of Colbourn et al. [J. Algorithms'96] -- whenever the input graph is sufficiently sparse. At a high level, our result stems from carefully exploiting the interplay of random spanning trees, random walks, and the notion of effective resistance, as well as from devising a way to algorithmically relate these concepts to the combinatorial structure of the graph. This involves, in particular, establishing a new connection between the effective resistance metric and the cut structure of the underlying graph.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jan 2015.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independencecited by 8
- 2021Local Algorithms for Estimating Effective Resistancecited by 6
- 2022Sparsification of the regularized magnetic Laplacian with multi-type spanning forestscited by 1
- 2026Efficient Edge Rewiring Strategies for Enhancing PageRank Fairnesscited by 1
- 2026Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness23 Jan 2026
- 2022Sparsification of the regularized magnetic Laplacian with multi-type spanning forests31 Aug 2022
- 2022Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence6 Apr 2022
- 2021Local Algorithms for Estimating Effective Resistance7 Jun 2021