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Mining Recurrent Patterns in a Dynamic Attributed Graph

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Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10235))

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Abstract

A great number of applications require to analyze a single attributed graph that changes over time. This task is particularly complex because both graph structure and attributes associated with each node can change. In the present work, we focus on the discovery of recurrent patterns in such a graph. These patterns are sequences of subgraphs which represent recurring evolutions of nodes w.r.t. their attributes. Various constraints have been defined and an original algorithm has been developed. Experiments performed on synthetic and real-world datasets have demonstrated the interest of our approach and its scalability.

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Author information

Authors and Affiliations

  1. PPME, University of New Caledonia, BP R4, 98851, Nouméa, New Caledonia

    Zhi Cheng, Frédéric Flouvat & Nazha Selmaoui-Folcher

Authors
  1. Zhi Cheng
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  2. Frédéric Flouvat
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  3. Nazha Selmaoui-Folcher
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Corresponding author

Correspondence to Nazha Selmaoui-Folcher .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Cheng, Z., Flouvat, F., Selmaoui-Folcher, N. (2017). Mining Recurrent Patterns in a Dynamic Attributed Graph. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_49

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  • DOI: https://doi.org/10.1007/978-3-319-57529-2_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57528-5

  • Online ISBN: 978-3-319-57529-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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