Finding the KT partition of a weighted graph in near-linear time

TL;DR

This paper presents a near-linear time randomized algorithm for finding the $(1+psilon)$-KT partition of a weighted graph, improving existing methods and enabling applications in minimum cut algorithms and graph representations.

Contribution

It introduces a novel near-linear time randomized algorithm for the $(1+psilon)$-KT partition, building on Karger's framework, with applications to minimum cut and graph representation.

Findings

Achieves near-linear time complexity for the $(1+psilon)$-KT partition.

Improves the time complexity of a quantum minimum cut algorithm.

Provides a new randomized algorithm for minimum cut with $O(m + n \u00b1  n)$ complexity.

Abstract

In a breakthrough work, Kawarabayashi and Thorup (J.~ACM'19) gave a near-linear time deterministic algorithm for minimum cut in a simple graph $G = (V,E)$. A key component is finding the $(1+\varepsilon)$-KT partition of $G$, the coarsest partition $\{P_1, \ldots, P_k\}$ of $V$ such that for every non-trivial $(1+\varepsilon)$-near minimum cut with sides $\{S, \bar{S}\}$ it holds that $P_i$ is contained in either $S$ or $\bar{S}$, for $i=1, \ldots, k$. Here we give a near-linear time randomized algorithm to find the $(1+\varepsilon)$-KT partition of a weighted graph. Our algorithm is quite different from that of Kawarabayashi and Thorup and builds on Karger's framework of tree-respecting cuts (J.~ACM'00). We describe applications of the algorithm. (i) The algorithm makes progress towards a more efficient algorithm for constructing the polygon representation of the set of near-minimum cuts in a graph. This is a generalization of the cactus representation initially described by Bencz\'ur (FOCS'95). (ii) We improve the time complexity of a recent quantum algorithm for minimum cut in a simple graph in the adjacency list model from $\widetilde O(n^{3/2})$ to $\widetilde O(\sqrt{mn})$. (iii) We describe a new type of randomized algorithm for minimum cut in simple graphs with complexity $O(m + n \log^6 n)$. For slightly dense graphs this matches the complexity of the current best $O(m + n \log^2 n)$ algorithm which uses a different approach based on random contractions. The key technical contribution of our work is the following. Given a weighted graph $G$ with $m$ edges and a spanning tree $T$, consider the graph $H$ whose nodes are the edges of $T$, and where there is an edge between two nodes of $H$ iff the corresponding 2-respecting cut of $T$ is a non-trivial near-minimum cut of $G$. We give a $O(m \log^4 n)$ time deterministic algorithm to compute a spanning forest of $H$.