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Temporal Flows in Temporal Networks

机译:时间网络中的时间流

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摘要

We introduce temporal flows on temporal networks [17,19], i.e., networks the links of which exist only at certain moments of time. Such networks are ephemeral in the sense that no link exists after some time. Our flow model is new and differs from the "flows over time" model, also called "dynamic flows" in the literature. We show that the problem of finding the maximum amount of flow that can pass from a source vertex s to a sink vertex t up to a given time is solvable in Polynomial time, even when node buffers are bounded. We then examine mainly the case of unbounded node buffers. We provide a simplified static Time-Extended network (STEG), which is of polynomial size to the input and whose static flow rates are equivalent to the respective temporal flow of the temporal network; using STEG, we prove that the maximum temporal flow is equal to the minimum temporal s-t cut. We further show that temporal flows can always be decomposed into flows, each of which moves only through a journey, i.e., a directed path whose successive edges have strictly increasing moments of existence. We partially characterise networks with random edge availabilities that tend to eliminate the s → t temporal flow. We then consider mixed temporal networks, which have some edges with specified availabilities and some edges with random availabilities; we show that it is #P-hard to compute the tails and expectations of the maximum temporal flow (which is now a random variable) in a mixed temporal network.
机译:我们在时间网络上介绍时间流[17,19],即网络只存在于某些时间矩的链接。这种网络是短暂的,因为没有一段时间没有链接。我们的流模型是新的,与“流量随时间”模型不同,在文献中也称为“动态流动”。我们表明,即使节点缓冲器被界定时,也可以在多项式时间中找到可以从源顶点S传递到给定时间的最大流量的问题。然后我们主要检查无限的节点缓冲区的情况。我们提供简化的静态时间扩展网络(STEG),该网络(STEG)是对输入的多项式大小,其静态流速相当于时间网络的各个时间流;使用STEG,我们证明了最大时间流量等于最小的时间S-T切割。我们进一步表明,时间流程可以始终分解成流量,每个流量只通过旅程移动,即,连续边缘具有严格增加存在时刻的定向路径。我们部分地表征了随机边缘可用性的网络,倾向于消除S→T时间流。然后,我们考虑混合时间网络,这些网络具有一些具有指定可用性的边缘和一些具有随机可用性的边缘;我们表明它是#p-难以计算混合时间网络中最大时间流量(现在是随机变量)的尾部和期望。

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