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Query-Decision Regression Between Shortest Path and Minimum Steiner Tree

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

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Abstract

Considering a graph with unknown weights, can we find the shortest path for a pair of nodes if we know the minimal Steiner trees associated with some subset of nodes? That is, with respect to a fixed latent decision-making system (e.g., a weighted graph), we seek to solve one optimization problem (e.g., the shortest path problem) by leveraging information associated with another optimization problem (e.g., the minimal Steiner tree problem). In this paper, we study such a prototype problem called query-decision regression with task shifts, focusing on the shortest path problem and the minimum Steiner tree problem. We provide theoretical insights regarding the design of realizable hypothesis spaces for building scoring models, and present two principled learning frameworks. Our experimental studies show that such problems can be solved to a decent extent with statistical significance.

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Notes

  1. 1.

    https://github.com/cdslabamotong/QRTS.

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Acknowledgement

This project is supported in part by National Science Foundation under Award Career IIS-2144285.

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Authors and Affiliations

  1. University of Delaware, Newark, USA

    Guangmo Tong, Peng Zhao & Mina Samizadeh

Authors
  1. Guangmo Tong
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  2. Peng Zhao
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  3. Mina Samizadeh
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Corresponding author

Correspondence to Guangmo Tong .

Editor information

Editors and Affiliations

  1. Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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Tong, G., Zhao, P., Samizadeh, M. (2024). Query-Decision Regression Between Shortest Path and Minimum Steiner Tree. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14646. Springer, Singapore. https://doi.org/10.1007/978-981-97-2253-2_25

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  • DOI: https://doi.org/10.1007/978-981-97-2253-2_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2252-5

  • Online ISBN: 978-981-97-2253-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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