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International Conference on Database Systems for Advanced Applications

DASFAA 2023: Database Systems for Advanced Applications pp 234–251Cite as

Efficient and Scalable Distributed Graph Structural Clustering at Billion Scale

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13945))

Abstract

Structural Graph Clustering (SCAN) is a fundamental problem in graph analysis and has received considerable attention recently. Existing distributed solutions either lack efficiency or suffer from high memory consumption when addressing this problem in billion-scale graphs. Motivated by these, in this paper, we aim to devise a distributed algorithm for SCAN that is both efficient and scalable. We first propose a fine-grained clustering framework tailored for SCAN. Based on the new framework, we devise a distributed SCAN algorithm, which not only keeps a low communication overhead during execution, but also effectively reduces the memory consumption at all time. We also devise an effective workload balance mechanism that is automatically triggered by the idle machines to handle skewed workloads. The experiment results demonstrate the efficiency and scalability of our proposed algorithm.

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Acknowledgements

Long Yuan is supported by National Key RD Program of China 2022YFF0712100, NSFC61902184, and Science and Technology on Information Systems Engineering Laboratory WDZC20205250411.

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Authors and Affiliations

  1. The University of New South Wales, Sydney, Australia

    Kongzhang Hao, Zhengyi Yang & Wenjie Zhang

  2. Nanjing University of Science and Technology, Nanjing, China

    Long Yuan

  3. Shanghai Jiao Tong University, Shanghai, China

    Xuemin Lin

Authors
  1. Kongzhang Hao
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  2. Long Yuan
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  3. Zhengyi Yang
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  4. Wenjie Zhang
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  5. Xuemin Lin
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Corresponding author

Correspondence to Kongzhang Hao .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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Cite this paper

Hao, K., Yuan, L., Yang, Z., Zhang, W., Lin, X. (2023). Efficient and Scalable Distributed Graph Structural Clustering at Billion Scale. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13945. Springer, Cham. https://doi.org/10.1007/978-3-031-30675-4_16

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  • DOI: https://doi.org/10.1007/978-3-031-30675-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30674-7

  • Online ISBN: 978-3-031-30675-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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