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Dynamic O(Arboricity) Coloring in Polylogarithmic Worst-Case Time

Published: 11 June 2024 Publication History

Abstract

A recent work by Christiansen, Nowicki, and Rotenberg [STOC’23] provides dynamic algorithms for coloring sparse graphs, concretely as a function of the graph’s arboricity α. They give two randomized algorithms: O(α logα) implicit coloring in poly(logn) worst-case update and query times, and O(min{α logα, α logloglogn}) implicit coloring in poly(logn) amortized update and query times (against an oblivious adversary). We improve these results in terms of the number of colors and the time guarantee: First, we present an extremely simple algorithm that computes an O(α)-implicit coloring with poly(logn) amortized update and query times. Second, and as the main technical contribution of our work, we show that the time complexity guarantee can be strengthened from amortized to worst-case. That is, we give a dynamic algorithm for implicit O(α)-coloring with poly(logn) worst-case update and query times (against an oblivious adversary).

References

[1]
Sepehr Assadi, Yu Chen, and Sanjeev Khanna. 2019. Sublinear algorithms for (Δ+ 1) vertex coloring. In ACM-SIAM Symposium on Discrete Algorithms (SODA). 767-786.
[2]
Luis Barba, Jean Cardinal, Matias Korman, Stefan Langerman, André Van Renssen, Marcel Roelofzen, and Sander Verdonschot. 2019. Dynamic graph coloring. Algorithmica 81 ( 2019 ), 1319-1341.
[3]
Leonid Barenboim, Michael Elkin, Seth Pettie, and Johannes Schneider. 2016. The Locality of Distributed Symmetry Breaking. Journal of the ACM (JACM) 63, 3, Article 20 (jun 2016 ), 45 pages.
[4]
József Beck. 1991. An algorithmic approach to the Lovász local lemma. I. Random Structures & Algorithms 2, 4 ( 1991 ), 343-365.
[5]
Sayan Bhattacharya, Fabrizio Grandoni, Janardhan Kulkarni, Quanquan C Liu, and Shay Solomon. 2022. Fully Dynamic (Δ+ 1)-Coloring in O (1) Update Time. ACM Transactions on Algorithms (TALG) 18, 2 ( 2022 ), 1-25.
[6]
Yi-Jun Chang, Manuela Fischer, Mohsen Ghafari, Jara Uitto, and Yufan Zheng. 2019. The complexity of (Δ+ 1) coloring in congested clique, massively parallel computation, and centralized local computation. In ACM Symposium on Principles of Distributed Computing (PODC). 471-480.
[7]
Yi-Jun Chang, Wenzheng Li, and Seth Pettie. 2018. An optimal distributed (Δ+ 1)-coloring algorithm?. In ACM Symposium on Theory of Computing (STOC). 445-456.
[8]
Aleksander BG Christiansen, Jacob Holm, Ivor van der Hoog, Eva Rotenberg, and Chris Schwiegelshohn. 2024. Adaptive Out-Orientations with Applications. In ACM Symposium on Discrete Algorithms (SODA). to appear, preprint arXiv: 2209. 14087.
[9]
Aleksander B. G. Christiansen, Krzysztof Nowicki, and Eva Rotenberg. 2022. Improved Dynamic Colouring of Sparse Graphs. In ACM Symposium on Theory of Computing (STOC). 1201-1214, arXiv: 2211.06858. https://doi.org/10.48550/ ARXIV.2211.06858
[10]
Richard Cole and Uzi Vishkin. 1986. Deterministic coin tossing with applications to optimal parallel list ranking. Information and Control 70, 1 ( 1986 ), 32-53.
[11]
Mohsen Ghafari and Fabian Kuhn. 2022. Deterministic distributed vertex coloring: Simpler, faster, and without network decomposition. In IEEE Symposium on Foundations of Computer Science (FOCS). 1009-1020.
[12]
Andrew Goldberg, Serge Plotkin, and Gregory Shannon. 1987. Parallel symmetrybreaking in sparse graphs. In ACM symposium on Theory of computing (STOC). 315-324.
[13]
Monika Henzinger, Stefan Neumann, and Andreas Wiese. 2020. Explicit and implicit dynamic coloring of graphs with bounded arboricity. arXiv preprint arXiv: 2002. 10142 ( 2020 ).
[14]
Nathan Linial. 1992. Locality in distributed graph algorithms. SIAM Journal on computing (SICOMP) 21, 1 ( 1992 ), 193-201.
[15]
Michael Luby. 1985. A simple parallel algorithm for the maximal independent set problem. In ACM symposium on Theory of computing (STOC). 1-10.
[16]
David W Matula and Leland L Beck. 1983. Smallest-last ordering and clustering and graph coloring algorithms. Journal of the ACM (JACM) 30, 3 ( 1983 ), 417-427.
[17]
C St JA Nash-Williams. 1964. Decomposition of finite graphs into forests. Journal of the London Mathematical Society 1, 1 ( 1964 ), 12-12.
[18]
David Zuckerman. 2006. Linear degree extractors and the inapproximability of max clique and chromatic number. In ACM symposium on Theory of computing (STOC). 681-690.

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    cover image ACM Conferences
    STOC 2024: Proceedings of the 56th Annual ACM Symposium on Theory of Computing
    June 2024
    2049 pages
    ISBN:9798400703836
    DOI:10.1145/3618260
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    Published: 11 June 2024

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    1. Coloring
    2. Dynamic Algorithms

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    June 24 - 28, 2024
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