Distributed Minimum Cut Approximation
Mohsen Ghaffari, Fabian Kuhn

TL;DR
This paper presents new distributed algorithms for approximating minimum cuts in graphs, achieving near-optimal time complexities and extending lower bounds to unweighted graphs, thus addressing open questions in distributed graph algorithms.
Contribution
It introduces two distributed algorithms for approximate minimum cut computation with near-optimal round complexities and extends lower bounds to unweighted graphs, solving open problems.
Findings
Algorithms achieve near-optimal round complexity bounds.
Lower bounds are extended to unweighted graphs.
The algorithms nearly match the theoretical lower bounds.
Abstract
We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, bits can be transmitted over each edge (a.k.a. the CONGEST model). We present a distributed algorithm that, for any weighted graph and any , with high probability finds a cut of size at most in rounds, where is the size of the minimum cut. This algorithm is based on a simple approach for analyzing random edge sampling, which we call the random layering technique. In addition, we also present another distributed algorithm, which is based on a centralized algorithm due to Matula [SODA '93], that with high probability computes a cut of size at most in rounds for…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Optimization and Search Problems · Interconnection Networks and Systems
