Near-Optimal Distributed Computation of Small Vertex Cuts
Merav Parter, Asaf Petruschka

TL;DR
This paper introduces near-optimal distributed algorithms in the CONGEST model for detecting small vertex cuts, overcoming previous degree dependency barriers and improving the efficiency of identifying cut vertices and pairs.
Contribution
The authors develop the first near-linear time distributed algorithms for computing all cut vertices and pairs, bypassing the degree dependency barrier in the CONGEST model.
Findings
Achieved an $ ilde{O}(D)$-round algorithm for all cut vertices.
Improved the algorithm for all cut pairs to $ ilde{O}(D)$-rounds.
Provided tools for omitting degree dependency in larger cut problems.
Abstract
We present near-optimal algorithms for detecting small vertex cuts in the CONGEST model of distributed computing. Despite extensive research in this area, our understanding of the vertex connectivity of a graph is still incomplete, especially in the distributed setting. To this date, all distributed algorithms for detecting cut vertices suffer from an inherent dependency in the maximum degree of the graph, . Hence, in particular, there is no truly sub-linear time algorithm for this problem, not even for detecting a single cut vertex. We take a new algorithmic approach for vertex connectivity which allows us to bypass the existing barrier. As a warm-up to our approach, we show a simple -round randomized algorithm for computing all cut vertices in a -diameter -vertex graph. This improves upon the -round algorithm of [Pritchard…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Complexity and Algorithms in Graphs · Cryptography and Data Security
