# Deep Distance Sensitivity Oracles

arXiv: https://arxiv.org/abs/2211.02681 · HTML: https://mlsanity.com/paper/2211.02681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training](https://mlsanity.com/paper/2009.03294/) (2020)
- [What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization](https://mlsanity.com/paper/2201.10494/) (2022)
- [A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip](https://mlsanity.com/paper/1710.04350/) (2017)
- [HAWK: Rapid Android Malware Detection through Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2108.07548/) (2021)
- [Shortest path distance approximation using deep learning techniques](https://mlsanity.com/paper/2002.05257/) (2020)
- [N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network](https://mlsanity.com/paper/1904.12605/) (2019)
- [Improved distance sensitivity oracles via tree partitioning](https://mlsanity.com/paper/1605.04491/) (2016)
- [Tight Hardness for Shortest Cycles and Paths in Sparse Graphs](https://mlsanity.com/paper/1712.08147/) (2017)
- [Improved Distance Sensitivity Oracles with Subcubic Preprocessing Time](https://mlsanity.com/paper/2007.11495/) (2020)
- [Constructing a Distance Sensitivity Oracle in O(n2.5794M) Time](https://mlsanity.com/paper/2102.08569/) (2021)

## Abstract (arXiv)

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.