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# Improved Hardness of Approximation of Diameter in the CONGEST Model

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LIPIcs.DISC.2020.19.pdf
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## Acknowledgements

We thank Noga Alon for useful pointers regarding generalized polygons. This work was done while the fist two authors were visiting the Simon’s Institute for the Theory of Computing.

## Cite As

Ofer Grossman, Seri Khoury, and Ami Paz. Improved Hardness of Approximation of Diameter in the CONGEST Model. In 34th International Symposium on Distributed Computing (DISC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 179, pp. 19:1-19:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.DISC.2020.19

## Abstract

We study the problem of approximating the diameter D of an unweighted and undirected n-node graph in the congest model. Through a connection to extremal combinatorics, we show that a (6/11 + ε)-approximation requires Ω(n^{1/6}/log n) rounds, a (4/7 + ε)-approximation requires Ω(n^{1/4}/log n) rounds, and a (3/5 + ε)-approximation requires Ω(n^{1/3}/log n) rounds. These lower bounds are robust in the sense that they hold even against algorithms that are allowed to return an additional small additive error. Prior to our work, only lower bounds for (2/3 + ε)-approximation were known [Frischknecht et al. SODA 2012, Abboud et al. DISC 2016]. Furthermore, we prove that distinguishing graphs of diameter 3 from graphs of diameter 5 requires Ω(n/log n) rounds. This stands in sharp contrast to previous work: while there is an algorithm that returns an estimate ⌊ 2/3D ⌋ ≤ D̃ ≤ D in Õ(√n+D) rounds [Holzer et al. DISC 2014], our lower bound implies that any algorithm for returning an estimate 2/3D ≤ D̃ ≤ D requires ̃Ω(n) rounds.

## Subject Classification

##### ACM Subject Classification
• Mathematics of computing → Approximation algorithms
• Theory of computation → Distributed computing models
##### Keywords
• Distributed graph algorithms
• Approximation algorithms
• Lower bounds

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## References

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