Recursive Random Contraction Revisited
David R. Karger, David P. Williamson · Society for Industrial and Applied Mathematics eBooks · 2021
In this note, we revisit the recursive random contraction algorithm of Karger and Stein [4] for finding a minimum cut in a graph. Our revisit is occasioned by a paper of Fox, Panigrahi, and Zhang [1] which gives an extension of the Karger-Stein algorithm to minimum cuts and minimum k-cuts in hypergraphs. When specialized to the case of graphs, the algorithm is somewhat different than the original Karger-Stein algorithm. We show that the analysis becomes particularly clean in this case: we can prove that the probability that a fixed minimum cut in an n node graph is returned by the algorithm is bounded below by 1/(2Hn – 2), where Hn is the nth harmonic number. We also consider other similar variants of the algorithm, and show that no such algorithm can achieve an asymptotically better probability of finding a fixed minimum cut.