Learning Shortest Paths on Large Dynamic Graphs

Yin, Jiaming; Rao, Weixiong; Zhang, Chenxi

doi:10.1109/mdm52706.2021.00040

Cited by 3 publications

(2 citation statements)

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“…Using the above methods to query for the shortest path length, routing algorithms can be constructed by performing a breadth-first search, where at each step only neighbors whose distance to the target is close to the current nodes' distance to the target are visited (Zhao et al 2011). Another framework finds the shortest path in the routing process, decomposing a large shortest path instance and solving it using reinforcement learning (Yin, Rao, and Zhang 2021). Though the accuracy of these routing algorithms does depend on their distance predictors, they remain limited in their efficiency as iterative algorithms require multiple predictions.…”

Section: Related Workmentioning

confidence: 99%

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Deep Distance Sensitivity Oracles

Jeong¹,

Pham²,

Bhakta³

et al. 2022

Preprint

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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). Such a DSO can be constructed, by utilizing the fact that the shortest path in a graph with failures consists of a concatenation of shortest paths in the original graph. That is, one can quickly compute a replacement path by determining a suitable pivot and concatenating the shortest paths from source to pivot and from pivot to target. Unfortunately, finding such a pivot is far from trivial. In this paper, we utilize node2vec, graph attention networks, and multi-layer perceptrons to find pivots such that the lengths of the resulting paths are close to that of the shortest replacement paths. More precisely, we evaluate our technique on a collection of real-world networks and observe that on average the computed replacement paths are longer by merely a few percentages compared to the optimal solution. 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. In particular, in this work we first prove the existence of a combinatorial structure and then build on top of it a deep learning algorithm that utilizes the observed combinatorial structure. As mentioned above -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.

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Section: Related Workmentioning

confidence: 99%

“…a wide range of real-world networks. In addition to outperforming other state of the art shortest paths works, our approach increases the functionality in two key ways(Yin, Rao, and Zhang 2021) (Qi et al 2020) …”

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confidence: 99%