计算时间图中的最大匹配

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
George B. Mertzios , Hendrik Molter , Rolf Niedermeier , Viktor Zamaraev , Philipp Zschoche
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引用次数: 0

摘要

时态图是拓扑结构随时间发生离散变化的图。给定静态底层图G,时间图通过将一组整数时间标签分配给G的每条边e来表示,指示e活动的离散时间步长。考虑到时间图的动态性质,我们引入并研究了经典图问题最大匹配的自然时间扩展的复杂性。在我们的问题“最大时间匹配”中,我们正在寻找尽可能多的时间标记边(简称时间边)(e,t),使得在Δ连续时隙的任何时间窗口内,没有顶点匹配超过一次,其中Δ∈N是给定的。我们证明了最大时间匹配的强计算硬度结果,即使是在基本情况下,以及相对于自然参数的固定参数算法和多项式时间近似算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing maximum matchings in temporal graphs

Temporal graphs are graphs whose topology is subject to discrete changes over time. Given a static underlying graph G, a temporal graph is represented by assigning a set of integer time-labels to every edge e of G, indicating the discrete time steps at which e is active. We introduce and study the complexity of a natural temporal extension of the classical graph problem Maximum Matching, taking into account the dynamic nature of temporal graphs. In our problem, Maximum Temporal Matching, we are looking for the largest possible number of time-labeled edges (simply time-edges) (e,t) such that no vertex is matched more than once within any time window of Δ consecutive time slots, where ΔN is given. We prove strong computational hardness results for Maximum Temporal Matching, even for elementary cases, as well as fixed-parameter algorithms with respect to natural parameters and polynomial-time approximation algorithms.

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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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