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SPAC: Scalable Pattern Approximate Counting in Graph Mining

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Algorithms and Architectures for Parallel Processing (ICA3PP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13777))

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Abstract

Pattern counting is a crucial task in graph pattern mining. Accurate counting is not affordable as the datasets grow larger and larger, and approximate counting is getting popular to provide an estimated answer quickly. However, current approximate counting approaches are still time-consuming and not scalable for extra-large graphs. This paper proposes SPAC, a fast and flexible pattern approximate counting method, based on the observation that pattern number distribution to degrees also follows power-law as the vertices, the common feature in graph datasets. By leveraging the distribution, SPAC can efficiently choose a small number of degrees as samples, fit the coefficients, and then calculate the pattern frequency directly. To provide flexibility for different use-cases, SPAC supports both accurate and approximate counting in the sampling phase. Moreover, edge weighting and interpolation techniques are adopted to emphasize the sample tail to improve fitting accuracy. The prototype of SPAC is implemented with GraphX on Spark, and is evaluated against various well-known graphs. The experimental results show that SPAC is up to 10x faster than accurate counting, keeping the same error level below 10%. Compared to existing approximate counting, SPAC is 1.4x–9x faster in general, while the error could be reduced to 20% of the current systems.

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Acknowledgement

This research is partially supported by National Key Research and Development Program of China with ID 2018AAA0103203 and PCL Peng Cheng Cloud Brain with ID PCL2021A13. We thank Yingchun Ma for valuable comments on early versions and we thank the reviewers for their insights.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Ruini Xue, Yijun Wang, Shengbo Liu, Yunxiang Li & Wenhong Tian

  2. Tsinghua University, Beijing, China

    Weimin Zheng

  3. Peng Cheng Lab (PCL), Shenzhen, China

    Ruini Xue & Weimin Zheng

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  1. Ruini Xue
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Correspondence to Ruini Xue .

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Editors and Affiliations

  1. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  2. University of New Brunswick, Fredericton, NB, Canada

    Rongxing Lu

  3. University of Exeter, Exeter, UK

    Geyong Min

  4. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

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Xue, R., Wang, Y., Liu, S., Li, Y., Tian, W., Zheng, W. (2023). SPAC: Scalable Pattern Approximate Counting in Graph Mining. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2022. Lecture Notes in Computer Science, vol 13777. Springer, Cham. https://doi.org/10.1007/978-3-031-22677-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-22677-9_12

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