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Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow

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New Frontiers in Artificial Intelligence (JSAI-isAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9067))

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Abstract

Detecting anomalies from structured graph data is becoming a critical task for many applications such as an analysis of disease infection in communities. To date, however, there exists no efficient method that works on massive attributed graphs with millions of vertices for detecting anomalous subgraphs with an abnormal distribution of vertex attributes. Here we report that this task is efficiently solved using the recent graph cut-based formulation. In particular, the full hierarchy of anomalous subgraphs can be simultaneously obtained via the parametric flow algorithm, which allows us to introduce the size constraint on anomalous subgraphs. We thoroughly examine the method using various sizes of synthetic and real-world datasets and show that our method is more than five orders of magnitude faster than the state-of-the-art method and is more effective in detection of anomalous subgraphs.

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Notes

  1. 1.

    This fact is pointed out in [26] but has not been used in any applications. A related result is theoretically analyzed in [16].

  2. 2.

    Source code is available at http://research.microsoft.com/en-us/downloads/d3adb5f7-49ea-4170-abde-ea0206b25de2/. Since the code can handle only integers for parameters, we first transform every parameter to an integer by multiplying some constant value.

  3. 3.

    http://www.cais.ntu.edu.sg/~chi/software.html.

  4. 4.

    http://www.cs.umd.edu/~sen/lbc-proj/LBC.html.

  5. 5.

    http://snap.stanford.edu/index.html.

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Acknowledgment

The authors thank Yoshinobu Kawahara for insightful discussions. This work was partially supported by JSPS KAKENHI 26880013 and Grand-in-Aid for JSPS Fellows 26-4555.

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Authors and Affiliations

  1. ISIR, Osaka University, 8-1, Mihogaoka, Ibaraki-shi, Osaka, 567-0047, Japan

    Mahito Sugiyama

  2. JST, PRESTO, Chiyoda-ku, Japan

    Mahito Sugiyama

  3. Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan

    Keisuke Otaki

Authors
  1. Mahito Sugiyama
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  2. Keisuke Otaki
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Correspondence to Mahito Sugiyama .

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Editors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Tsuyoshi Murata

  2. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  3. Department Information Science, Faculty of Science, Ochanomizu University , Tokyo, Japan

    Daisuke Bekki

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Sugiyama, M., Otaki, K. (2015). Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow. In: Murata, T., Mineshima, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2014. Lecture Notes in Computer Science(), vol 9067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48119-6_26

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  • DOI: https://doi.org/10.1007/978-3-662-48119-6_26

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  • Print ISBN: 978-3-662-48118-9

  • Online ISBN: 978-3-662-48119-6

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