Practical Minimum Cut Algorithms
Abstract
The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges. Here, we introduce a linear-time algorithm to compute near-minimum cuts. Our algorithm is based on cluster contraction using label propagation and Padberg and Rinaldi's contraction heuristics [SIAM Review, 1991]. We give both sequential and shared-memory parallel implementations of our algorithm. Extensive experiments on both real-world and generated instances show that our algorithm finds the optimal cut on nearly all instances significantly faster than other state-of-the-art algorithms while our error rate is lower than that of other heuristic algorithms. In addition, our parallel algorithm shows good scalability.
Cite
@article{arxiv.1708.06127,
title = {Practical Minimum Cut Algorithms},
author = {Monika Henzinger and Alexander Noe and Christian Schulz and Darren Strash},
journal= {arXiv preprint arXiv:1708.06127},
year = {2019}
}