The complexity of transitively orienting temporal graphs

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2025-02-03 DOI:10.1016/j.jcss.2025.103630
George B. Mertzios , Hendrik Molter , Malte Renken , Paul G. Spirakis , Philipp Zschoche
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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.
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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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