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Diversified Pattern Mining on Large Graphs

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Database and Expert Systems Applications (DEXA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12923))

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Abstract

Frequent pattern mining (\(\mathsf {FPM}\)) on large graph has been receiving increasing attention due to its wide applications. The \(\mathsf {FPM}\) problem is defined as mining all the subgraphs (a.k.a. patterns), with frequency above a user-defined threshold in a large graph. Though a host of techniques have been developed, most of them suffers from high computational cost and inconvenient result inspection. To tackle the issues, we propose an approach to discover diversified top-k patterns from a large graph G. We formalize the distributed top-k pattern mining problem based on a diversification function. We develop an algorithm with early termination property, to efficiently identify diversified top-k patterns. Using real-life and synthetic graphs, we show advantages of our algorithm via intensive experimental studies.

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Author information

Authors and Affiliations

  1. Southwest Petroleum University, Chengdu, China

    Xin Wang, Liang Tang & Yong Liu

  2. Sichuan ChangHong Electric Co. Ltd., Mianyang, China

    Huayi Zhan

  3. Sichuan University, Chengdu, China

    Xuanzhe Feng

Authors
  1. Xin Wang
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  2. Liang Tang
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  3. Yong Liu
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  4. Huayi Zhan
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  5. Xuanzhe Feng
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Corresponding author

Correspondence to Xin Wang .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  3. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  4. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Wang, X., Tang, L., Liu, Y., Zhan, H., Feng, X. (2021). Diversified Pattern Mining on Large Graphs. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham. https://doi.org/10.1007/978-3-030-86472-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-86472-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86471-2

  • Online ISBN: 978-3-030-86472-9

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