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Fast subgraph query processing and subgraph matching via static and dynamic equivalences

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Abstract

Subgraph query processing (also known as subgraph search) and subgraph matching are fundamental graph problems in many application domains. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we propose a new subgraph search algorithm using equivalences of vertices in order to reduce search space: (1) static equivalence of vertices in a query graph that leads to an efficient matching order of the vertices and (2) dynamic equivalence of candidate vertices in a data graph, which enables us to capture and remove redundancies in search space. These techniques for subgraph search also lead to an improved algorithm for subgraph matching. Experiments show that our approach outperforms state-of-the-art subgraph search and subgraph matching algorithms by up to several orders of magnitude with respect to query processing time.

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Notes

  1. https://github.com/SNUCSE-CTA/VEQ

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Acknowledgements

Hyunjoon Kim was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)) and the research fund of Hanyang University (HY-202100000003161). Kunsoo Park was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00551, Framework of Practical Algorithms for NP-hard Graph Problems). Wook-Shin Han was supported by the National Research Foundation of Korea (NRF) grant (No. NRF-2021R1A2B5B03001551) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant (No. 2018-0-01398).

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Authors and Affiliations

  1. Department of Data Science, Hanyang University, Seoul, South Korea

    Hyunjoon Kim

  2. Department of Artificial Intelligence, Hanyang University, Seoul, South Korea

    Hyunjoon Kim

  3. Kyungwontech, Seoul, South Korea

    Yunyoung Choi

  4. Seoul National University, Seoul, South Korea

    Kunsoo Park

  5. University of New South Wales, Kensington, Australia

    Xuemin Lin

  6. University of Sydney, Sydney, Australia

    Seok-Hee Hong

  7. Pohang University of Science and Technology (POSTECH), Pohang, South Korea

    Wook-Shin Han

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  1. Hyunjoon Kim
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Correspondence to Kunsoo Park or Wook-Shin Han.

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Seok-Hee Hong works in the same university as Alan Fekete of the editorial board. Wook-Shin Han has a conflict of interest with Kyu-Young Whang in the editorial board.

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A preliminary version [19] of this paper was presented at Proceedings of the 2021 International Conference on Management of Data (SIGMOD 2021)

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Kim, H., Choi, Y., Park, K. et al. Fast subgraph query processing and subgraph matching via static and dynamic equivalences. The VLDB Journal 32, 343–368 (2023). https://doi.org/10.1007/s00778-022-00749-x

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  • DOI: https://doi.org/10.1007/s00778-022-00749-x

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