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The research focuses on finding efficient methods for estimating the maximum matching size in low-arboricity graphs within a dynamic graph stream model.

March 16, 2026Open Access

Matchings in Low-Arboricity Graphs in the Dynamic Graph Stream Model

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The research focuses on finding efficient methods for estimating the maximum matching size in low-arboricity graphs within a dynamic graph stream model.
Developed a three-pass streaming algorithm for (α + 2)(1 + ε)-approximation with specific space requirements.
Introduced a two-pass algorithm that has reduced space complexity compared to one-pass methods.
Created a (1+ε)-approximation multi-pass algorithm, where space depends on a known upper bound for the largest matching size.
The three-pass algorithm achieves an approximation with space O(ε^{-2}⋅α²⋅n^{1/2}⋅log n).
Identified an Ω(α^{-5/2}⋅n^{1/2}) space lower bound for one-pass algorithms.
The two-pass algorithm operates with space O(ε^{-2}⋅α²⋅n^{3/5}⋅log n).

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Implication

Demonstrates efficient algorithms for estimating maximum matchings in low-arboricity graphs, suggesting improved approximations and space usage in dynamic settings.

Cite This Study

Konrad et al. (2024) studied this question.

synapsesocial.com/papers/69b79e638166e15b153ab9bd https://doi.org/10.4230/lipics.fsttcs.2024.29

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Implication

Demonstrates efficient algorithms for estimating maximum matchings in low-arboricity graphs, suggesting improved approximations and space usage in dynamic settings.

Matchings in Low-Arboricity Graphs in the Dynamic Graph Stream Model

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Implication

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Matchings in Low-Arboricity Graphs in the Dynamic Graph Stream Model

Key Points

Discussion

Implication

Cite This Study

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