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International Conference on Web Information Systems Engineering

WISE 2018: Web Information Systems Engineering – WISE 2018 pp 348–363Cite as

Renovating Watts and Strogatz Random Graph Generation by a Sequential Approach

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11233))

Abstract

Numerous data intensive applications call for generating gigantic random graphs. The Watts-Strogatz model is well noted as a fundamental, versatile yet simple random graph model. The Watts-Strogatz model simulates the “small world” phenomenon in real-world graphs that includes short average path lengths and high clustering. However, the existing algorithms for the Watts-Strogatz model are not scalable. This study proposes a sequential algorithm termed ZWS that generates exact Watts-Strogatz graphs with fewer iterations than the state-of-the-art Watts-Strogatz algorithm and therefore faster. Given the so-called edge rewiring probability p (\(0 \le p \le 1\)) of the Watts-Strogatz model and m neighbouring nodes of v nodes, ZWS needs \(p \times m \times v\) random decisions while the state-of-the-art Watts-Strogatz algorithm needs \(m \times v\), such that for large graphs with small probability, ZWS is able to generate Watts-Strogatz graphs with substantially less iterations. However, the less iterations in ZWS requires complex computation which avoids ZWS to achieve its full practical speedup. Therefore, we further improve our solution as PreZWS that enhances ZWS algorithm through pre-computation techniques to substantially speedup the overall generation process practically. Extensive experiments show the efficiency and effectiveness of the proposed scheme, e.g., PreZWS yields average speedup of 2 times over the state-of-the-art algorithm on a single machine.

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Acknowledgements

The work was partially supported by the CAS Pioneer Hundred Talents Program under grant number Y84402.

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Authors and Affiliations

  1. Rakuten, Inc., Tokyo, Japan

    Sadegh Nobari

  2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China

    Qiang Qu, Muhammad Muzammal & Qingshan Jiang

Authors
  1. Sadegh Nobari
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  2. Qiang Qu
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  3. Muhammad Muzammal
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  4. Qingshan Jiang
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Corresponding author

Correspondence to Qiang Qu .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. University of Victoria, Footscray, VIC, Australia

    Hua Wang

  4. UNSW Australia, Sydney, NSW, Australia

    Hye-Young Paik

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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Nobari, S., Qu, Q., Muzammal, M., Jiang, Q. (2018). Renovating Watts and Strogatz Random Graph Generation by a Sequential Approach. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-02922-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02921-0

  • Online ISBN: 978-3-030-02922-7

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