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Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure

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Mobile Computing, Applications, and Services (MobiCASE 2021)

Abstract

Community detection has been widely studied from many different perspectives, which include heuristic approaches in the past and graph neural network in recent years. With increasing security and privacy concerns, community detectors have been demonstrated to be vulnerable. A slight perturbation to the graph data can greatly change the detection results. In this paper, we focus on dealing with a kind of attack on one of the communities by manipulating the graph structure. We formulate this case as target community problem. The big challenge to solve this problem is the universality on different detectors. For this, we define structural information gain (SIG) to guide the manipulation and design an attack algorithm named SIGM. We compare SIGM with some recent attacks on five graph datasets. Results show that our attack is effective on misleading community detector.

This paper is supported by National Natural Science Foundation of China No. 62172040, No. U1836212, No. 61872041.

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Notes

  1. 1.

    https://deim.urv.cat/~alexandre.arenas/data/welcome.htm.

  2. 2.

    http://snap.stanford.edu/data/.

  3. 3.

    https://paperswithcode.com/datasets?mod=graphs.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Kaibin Wan & Yiwei Liu

  2. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China

    Zijian Zhang

  3. School of Computer Science, The University of Auckland, Auckland, New Zealand

    Jiamou Liu & Zijian Zhang

  4. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Bakhadyr Khoussainov

Authors
  1. Kaibin Wan
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  2. Jiamou Liu
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  3. Yiwei Liu
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  4. Zijian Zhang
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  5. Bakhadyr Khoussainov
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Corresponding author

Correspondence to Zijian Zhang .

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Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Shuiguang Deng

  2. University of Sydney, Sydney, NSW, Australia

    Albert Zomaya

  3. Beijing Information Science and Technology University, Beijing, China

    Ning Li

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wan, K., Liu, J., Liu, Y., Zhang, Z., Khoussainov, B. (2022). Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure. In: Deng, S., Zomaya, A., Li, N. (eds) Mobile Computing, Applications, and Services. MobiCASE 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 434. Springer, Cham. https://doi.org/10.1007/978-3-030-99203-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-99203-3_8

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  • Online ISBN: 978-3-030-99203-3

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