The complexity of transitively orienting temporal graphs

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
George B. Mertzios , Hendrik Molter , Malte Renken , Paul G. Spirakis , Philipp Zschoche
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引用次数: 0

Abstract

In a temporal network with discrete time-labels on its edges, information can only “flow” along sequences of edges with non-decreasing (resp. increasing) time-labels. In this paper we make a first attempt to understand how the direction of information flow on one edge can impact the direction of information flow on other edges. By naturally extending the classical notion of a transitive orientation in static graphs, we introduce the fundamental notion of a temporal transitive orientation, and we systematically investigate its algorithmic behavior. Our main result is a conceptually simple, yet technically quite involved, polynomial-time algorithm for recognizing whether a temporal graph G is transitively orientable. In wide contrast we prove that, surprisingly, it is NP-hard to recognize whether G is strictly transitively orientable. Additionally we introduce further related problems to temporal transitivity, notably among them the temporal transitive completion problem, for which we prove both algorithmic and hardness results.
传递取向时间图的复杂性
在边缘上有离散时间标签的时间网络中,信息只能沿非递减的边缘序列“流动”。增加时间。在本文中,我们首次尝试理解一个边缘上的信息流方向如何影响其他边缘上的信息流方向。通过自然地扩展静态图中传递取向的经典概念,我们引入了时间传递取向的基本概念,并系统地研究了它的算法行为。我们的主要成果是一个概念上简单,但技术上相当复杂的多项式时间算法,用于识别时间图G是否可传递定向。在广泛的对比中,我们证明了,令人惊讶的是,识别G是否严格传递可定向是np困难的。此外,我们进一步介绍了与时间传递性相关的问题,特别是其中的时间传递补全问题,我们证明了算法和硬度结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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