Let $G=(V,E)$ be an $n$-nodes non-negatively real-weighted undirected graph.In this paper we show how to enrich a {\em single-source shortest-path tree}(SPT) of $G$ with a \emph{sparse} set of \emph{auxiliary} edges selected from$E$, in order to create a structure which tolerates effectively a \emph{pathfailure} in the SPT. This consists of a simultaneous fault of a set $F$ of atmost $f$ adjacent edges along a shortest path emanating from the source, and itis recognized as one of the most frequent disruption in an SPT. We show that,for any integer parameter $k \geq 1$, it is possible to provide a very sparse(i.e., of size $O(kn\cdot f^{1+1/k})$) auxiliary structure that carefullyapproximates (i.e., within a stretch factor of $(2k-1)(2|F|+1)$) the trueshortest paths from the source during the lifetime of the failure. Moreover, weshow that our construction can be further refined to get a stretch factor of$3$ and a size of $O(n \log n)$ for the special case $f=2$, and that it can beconverted into a very efficient \emph{approximate-distance sensitivity oracle},that allows to quickly (even in optimal time, if $k=1$) reconstruct theshortest paths (w.r.t. our structure) from the source after a path failure,thus permitting to perform promptly the needed rerouting operations. Ourstructure compares favorably with previous known solutions, as we discuss inthe paper, and moreover it is also very effective in practice, as we assessthrough a large set of experiments.
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机译:令$ G =(V,E)$是一个$ n $个节点的非负实数无向图。在本文中,我们展示了如何丰富一个{\ em单源最短路径树}(SPT) $ G $具有从$ E $中选择的\ emph {稀疏}边\ emph {auxiliary}边的集合,以便创建一个有效地容忍SPT中的\ emph {pathfailure}的结构。这包括同时发生的故障,即沿源头发出的最短路径上的至多$ f $个相邻边的一组$ F $相邻故障,并且被认为是SPT中最频繁的中断之一。我们表明,对于任何整数参数$ k \ geq 1 $,可以提供非常稀疏(即,大小为$ O(kn \ cdot f ^ {1 + 1 / k})$)的辅助结构(即,在$(2k-1)(2 | F | +1)$的拉伸因子之内)失效期间,从源头出发的真正最短路径。此外,我们表明我们的构造可以进一步细化,以得到特殊情况$ f = 2 $的拉伸因子$ 3 $和大小$ O(n \ log n)$,并且可以将其转换为非常有效的\ emph {近似距离敏感度oracle},允许在路径失败后快速(即使在最佳时间内,如果$ k = 1 $)从源重构最短路径(破坏了我们的结构),因此可以迅速执行所需的操作重新路由操作。正如我们在本文中讨论的那样,我们的结构与以前已知的解决方案相比具有优势,此外,由于我们通过大量实验进行评估,我们的结构在实践中也非常有效。
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