One of the most fundamental graph problems is finding a shortest path from a source to a target node. While in its basic forms the problem has been studied extensively and efficient algorithms are known, it becomes significantly harder as soon as parts of the graph are susceptible to failure. Although one can recompute a shortest replacement path after every outage, this is rather inefficient both in time and/or storage. One way to overcome this problem is to shift computational burden from the queries into a pre-processing step, where a data structure is computed that allows for fast querying of replacement paths, typically referred to as a Distance Sensitivity Oracle (DSO). While DSOs have been extensively studied in the theoretical computer science community, to the best of our knowledge this is the first work to construct DSOs using deep learning techniques. We show how to use deep learning to utilize a combinatorial structure of replacement paths. More specifically, we utilize the combinatorial structure of replacement paths as a concatenation of shortest paths and use deep learning to find the pivot nodes for stitching shortest paths into replacement paths.

翻译：最基本的图问题之一是从源节点到目标节点寻找最短路径。尽管该问题的基础形式已被广泛研究且已知高效算法，但当图的某些部分容易发生故障时，问题会变得显著困难。虽然可以在每次故障后重新计算最短替换路径，但这种方法在时间和/或存储方面效率低下。解决此问题的一种方法是将计算负担从查询转移到预处理步骤，即预先计算一种数据结构，以便能够快速查询替换路径，这种数据结构通常称为距离敏感度预言机（DSO）。尽管DSO在理论计算机科学界已被广泛研究，但据我们所知，这是首次利用深度学习技术构建DSO的工作。我们展示了如何利用深度学习来利用替换路径的组合结构。具体而言，我们将替换路径的组合结构视为最短路径的串联，并利用深度学习来寻找用于将最短路径拼接成替换路径的枢轴节点。