A tight quasi-polynomial bound for Global Label Min-Cut

We study a generalization of the classic GLOBAL MIN-CUT problem, called GLOBAL LABEL MIN-CUT (or sometimes GLOBAL HEDGE MIN-CUT): the edges of the input (multi)graph are labeled (or partitioned into color classes or hedges), and removing all edges of the same label (color or from the same hedge) costs one. The problem asks to disconnect the graph at minimum cost. While the st-cut version of the problem is known to be NP-hard, the above global cut version is known to admit a quasi-polynomial randomized nO(log OPT)-time algorithm due to Ghaffari, Karger, and Panigrahi [SODA 2017]. They consider this as “strong evidence that this problem is in P”. We show that this is actually not the case. We complete the study of the complexity of the Global Label Min-Cut problem by showing that the quasi-polynomial running time is probably optimal: We show that the existence of an algorithm with running time (np)o(log n/(log log n)2) would contradict the Randomized Exponential Time Hypothesis, where n is the number of vertices, and p is the number of labels in the input. The key step for the lower bound is a proof that Global Label Min-Cut is W[1]-hard when parameterized by the number of uncut labels. In other words, the problem is difficult in the regime where almost all labels need to be cut to disconnect the graph. To turn this lower bound into a quasi-polynomial-time lower bound, we also needed to revisit the framework due to Marx [Theory Comput. 2010] of proving lower bounds assuming Exponential Time Hypothesis through the SUBGRAPH ISOMORPHISM problem parameterized by the number of edges of the pattern. Here, we provide an alternative simplified proof of the hardness of this problem that is more versatile with respect to the choice of the regimes of the parameters. * This research is a part of a project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme Grant Agreement 714704 (LJ, TM, MP, US) and from the Research Council of Norway (LJ).