Tree-Packing Revisited: Faster Fully Dynamic Min-Cut and Arboricity

Abstract

A tree-packing is a collection of spanning trees of a graph. It has been a useful tool for computing the minimum cut in static, dynamic, and distributed settings. In particular, [Thorup, Comb. 2007] used them to obtain his dynamic min-cut algorithm with $\tilde{O}({\lambda }^{14.5}\sqrt{n})$ worst-case update time. We reexamine this relationship, showing that we need to maintain fewer spanning trees for such a result; we show that we only need to pack $\Theta ({\lambda }^3\log m)$ greedy trees to guarantee a 1-respecting cut or a trivial cut in some contracted graph. Based on this structural result, we then provide a deterministic algorithm for fully dynamic exact min-cut, that has $\tilde{O}({\lambda }^{5.5}\sqrt{n})$ worst-case update time, for min-cut value bounded by $\lambda$. In particular, this also leads to an algorithm for general fully dynamic exact min-cut with $\tilde{O}(m^{1-1/12})$ amortized update time, improving upon $\tilde{O}(m^{1-1/31})$ [Goranci et al., SODA 2023]. We also give the first fully dynamic algorithm that maintains a $(1+\epsilon )$-approximation of the fractional arboricity -- which is strictly harder than the integral arboricity. Our algorithm is deterministic and has $O(\alpha {\log }^{6}m/{\epsilon }^4)$ amortized update time, for arboricity at most $\alpha$. We extend these results to a Monte Carlo algorithm with $O(\text{poly}(\log m,{\epsilon }^{-1}))$ amortized update time against an adaptive adversary. Our algorithms work on multi-graphs as well. Both result are obtained by exploring the connection between the min-cut/arboricity and (greedy) tree-packing. We investigate tree-packing in a broader sense; including a lower bound for greedy tree-packing, which - to the best of our knowledge - is the first progress on this topic since [Thorup, Comb. 2007].

Presented at SODA '25. Full version published in Algorithmica