Sampling Random Spanning Trees Faster than Matrix Multiplication
STOC, pp. 730-742, 2017.
We present an algorithm that, with high probability, generates a random spanning tree from an edge-weighted undirected graph in (n5/3 m1/3) time. The tree is sampled from a distribution where the probability of each tree is proportional to the product of its edge weights. This improves upon the previous best algorithm due to Colbourn et a...More
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