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arXiv 2404.03472cs.DSmath.CO

使用极大独立集查询进行图重建的下界

Lower bounds for graph reconstruction with maximal independent set queries

Lukas Michel, Alex Scott

AI总结:

本文研究极大独立集查询下的隐藏图重建,分别给出随机自适应、随机非自适应和确定性非自适应算法的查询次数下界,并改进无覆盖族的相关界。

摘要:

我们研究重建隐藏图的边所需的极大独立集查询次数。我们证明,对于最大度为 Δ 的 n 顶点图,随机自适应算法若要以至少 1/2 的成功概率完成重建,至少需要 Ω(Δ^2 log(n / Δ) / log Δ) 次查询;对于随机非自适应算法,我们进一步将该下界改进为 Ω(Δ^2 log(n / Δ))。我们还证明,确定性非自适应算法至少需要 Ω(Δ^3 log n / log Δ) 次查询。这改进了 Konrad、O'Sullivan 和 Traistaru 的界,并回答了他们提出的一个问题。确定性非自适应算法下界的证明依赖于与无覆盖族的联系,对此我们也改进了已知界。

英文摘要:

We investigate the number of maximal independent set queries required to reconstruct the edges of a hidden graph. We show that randomised adaptive algorithms need at least $Ω(Δ^2 \log(n / Δ) / \log Δ)$ queries to reconstruct $n$-vertex graphs of maximum degree $Δ$ with success probability at least $1/2$, and we further improve this lower bound to $Ω(Δ^2 \log(n / Δ))$ for randomised non-adaptive algorithms. We also prove that deterministic non-adaptive algorithms require at least $Ω(Δ^3 \log n / \log Δ)$ queries. This improves bounds of Konrad, O'Sullivan, and Traistaru, and answers one of their questions. The proof of the lower bound for deterministic non-adaptive algorithms relies on a connection to cover-free families, for which we also improve known bounds.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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