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Deep Distance Sensitivity Oracles

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Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1141))

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Abstract

Shortest path computation is one of the most fundamental and well-studied problems in algorithmic graph theory, though it becomes more complex when graph components are susceptible to failure. This research utilizes a Distance Sensitivity Oracle (DSO) for efficiently querying replacement paths in graphs with potential failures to avoid inefficiently recomputing them after every outage with traditional techniques. By leveraging technologies such as node2vec, graph attention networks, and multi-layer perceptrons, the study pioneers a method to identify pivot nodes that lead to replacement paths closely resembling optimal solutions with deep learning. Tests on real-world network demonstrate replacement paths that are longer by merely a few percentages compared to the optimal solution.

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Notes

  1. 1.

    For a non-negative function \(f = f(n)\), we use \(\tilde{O}(f)\) to denote \(O(f \cdot \textsf {polylog}(n))\).

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Author information

Authors and Affiliations

  1. Harvard University, Cambridge, MA, 02138, USA

    Davin Jeong

  2. Dartmouth College, Hanover, NH, 03755, USA

    Allison Gunby-Mann & Peter Chin

  3. Hasso Plattner Institute, Potsdam, Germany

    Sarel Cohen & Tobias Friedrich

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Maximilian Katzmann

  5. Boston University, Boston, MA, 02215, USA

    Chau Pham

  6. Yale University, New Haven, CT, 06520, USA

    Arnav Bhakta

Authors
  1. Davin Jeong
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  2. Allison Gunby-Mann
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  3. Sarel Cohen
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  4. Maximilian Katzmann
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  5. Chau Pham
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  6. Arnav Bhakta
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  7. Tobias Friedrich
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  8. Peter Chin
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Correspondence to Davin Jeong .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Sciences, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

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Jeong, D. et al. (2024). Deep Distance Sensitivity Oracles. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1141. Springer, Cham. https://doi.org/10.1007/978-3-031-53468-3_38

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  • DOI: https://doi.org/10.1007/978-3-031-53468-3_38

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