Local Flow Partitioning for Faster Edge Connectivity
Monika Henzinger, Satish Rao, Di Wang

TL;DR
This paper presents a new deterministic algorithm for computing minimum cuts in undirected graphs with improved running time, extending to balanced directed graphs, using a novel local flow-based method for finding low conductance cuts.
Contribution
It introduces a flow-based local algorithm for low conductance cuts, improving deterministic minimum cut computation and extending to balanced directed graphs.
Findings
Deterministic algorithm runs in O(m log^2 n log log^2 n) time.
Flow-based local method effectively finds low conductance cuts.
Extension to balanced directed graphs broadens applicability.
Abstract
We study the problem of computing a minimum cut in a simple, undirected graph and give a deterministic time algorithm. This improves both on the best previously known deterministic running time of (Kawarabayashi and Thorup, STOC 2015) and the best previously known randomized running time of (Karger, J.ACM 2000) for this problem, though Karger's algorithm can be further applied to weighted graphs. Moreover, our result extends to balanced directed graphs, where the balance of a directed graph captures how close the graph is to being Eulerian. Our approach is using the Kawarabayashi and Thorup graph compression technique, which repeatedly finds low-conductance cuts. To find these cuts they use a diffusion-based local algorithm. We use instead a flow-based local algorithm and suitably adjust their framework to work with our…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Optimization and Search Problems · Privacy-Preserving Technologies in Data
