Finding k-secluded trees faster

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-06-05 DOI:10.1016/j.jcss.2023.05.006
Huib Donkers, Bart M.P. Jansen , Jari J.H. de Kroon
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Abstract

We revisit the k-Secluded Tree problem. Given a vertex-weighted undirected graph G, its objective is to find a maximum-weight induced subtree T whose open neighborhood has size at most k. We present a fixed-parameter tractable algorithm that solves the problem in time 2O(klogk)nO(1), improving on a double-exponential running time from earlier work by Golovach, Heggernes, Lima, and Montealegre. Starting from a single vertex, our algorithm grows a k-secluded tree by branching on vertices in the open neighborhood of the current tree T. To bound the branching depth, we prove a structural result that can be used to identify a vertex that belongs to the neighborhood of any k-secluded supertree TT once the open neighborhood of T becomes sufficiently large. We extend the algorithm to enumerate compact descriptions of all maximum-weight k-secluded trees, which allows us to count them as well.

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更快地找到隐蔽的树
我们重新讨论k-封闭树问题。给定一个顶点加权无向图G,它的目标是找到一个开邻域大小最大为k的最大权诱导子树T⁡k) ·nO(1),在Golovach、Heggernes、Lima和Montealegre早期工作的双指数运行时间基础上改进。从单个顶点开始,我们的算法通过在当前树T的开邻域中的顶点上进行分支来生长k隐树。为了限制分支深度,我们证明了一个结构结果,一旦T的开域变得足够大,就可以用来识别属于任何k隐超树T′⊇T的邻域的顶点。我们将算法扩展到枚举所有最大权重k隐树的紧凑描述,这也允许我们对它们进行计数。
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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