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新论文 · v1arXiv:2611.05932

Faster Dynamic Maximal Independent Set against Adaptive Adversaries

Hongyi Chen, Shuyan Fan, Tianyi Zhang

原文摘要

We study the fully dynamic maximal independent set (MIS) problem: maintaining an MIS in an undirected graph on $n$ vertices with at most $m$ edges under edge insertions and deletions. Against an oblivious adversary, which fixes the edge update sequence in advance, the randomized algorithms of Behnezhad et al. (2019) and Chechik and Zhang (2019) achieve polylogarithmic update time. Against an adaptive adversary, which may choose each update after observing the previously reported MISs, the best previously known update bound was $O(m^{2/3})$, given by the deterministic algorithm of Gupta and Khan (2018, 2021). Assuming the combinatorial Boolean matrix multiplication hypothesis, Bernstein et al. (2026) proved that any combinatorial algorithm against an adaptive adversary requires at least $m^{1/2-o(1)}$ update time even under edge insertions alone, leaving a polynomial gap between upper and lower bounds. We present a randomized fully dynamic MIS algorithm with $\widetilde O(m^{5/8})=\widetilde O(m^{2/3-1/24})$ update time against adaptive adversaries. This gives the first polynomial improvement over the $m^{2/3}$ bound for adaptive fully dynamic MIS.

cs.DS

新论文 · v1arXiv:2610.40146

Nearly Optimal Sparse Navigable Graphs in General Metrics

Hongyi Chen, Tianyi Zhang

原文摘要

We study approximation algorithms for constructing sparse navigable graphs in general metrics. Given an $n$-point metric $(P,d)$ and a parameter $α\ge1$, a directed graph on $P$ is $α$-navigable if, for every pair of distinct points $s,t\in P$, the source $s$ has an outgoing neighbor $p$ with $d(p,t)<d(s,t)/α$. The objective is to minimize the number of edges. Recent independent works of Khanna, Padaki, and Waingarten (2026) and Conway et al. (2026) give $O(\log n)$-approximation algorithms and show that an $o(\log n)$ approximation is NP-hard in general. Before our work, the best known running time for such an approximation for general $α\ge1$ was $\widetilde O(\min\{n\operatorname{OPT}_α,n^{2.5}\})$, where $\operatorname{OPT}_α$ is the minimum number of edges in an $α$-navigable graph. In this paper, we give a randomized algorithm that, with high probability, runs in $\widetilde O(n^2)$ time and achieves an $O(\log n)$ approximation for every $α\ge1$. In general, these guarantees are nearly optimal up to constant factors in the approximation and polylogarithmic factors in the running time.

cs.DS