Deep Distance Sensitivity Oracles

TL;DR

This paper introduces a novel approach to constructing Distance Sensitivity Oracles (DSOs) using deep learning, enabling efficient replacement path queries in graphs with potential failures by leveraging combinatorial path structures.

Contribution

First to apply deep learning techniques to construct DSOs, utilizing combinatorial structures of replacement paths for improved efficiency.

Findings

Deep learning can effectively identify pivot nodes for path stitching.

The method improves query efficiency for shortest path replacements.

The approach demonstrates potential for scalable graph fault tolerance.

Abstract

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.