  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.
