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Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13–14, 2021, Proceedings

Research Article

Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure

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  • @INPROCEEDINGS{10.1007/978-3-030-99203-3_8,
        author={Kaibin Wan and Jiamou Liu and Yiwei Liu and Zijian Zhang and Bakhadyr Khoussainov},
        title={Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure},
        proceedings={Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13--14, 2021, Proceedings},
        proceedings_a={MOBICASE},
        year={2022},
        month={3},
        keywords={Adversarial community detection Graph neural network Structural entropy},
        doi={10.1007/978-3-030-99203-3_8}
    }
    
  • Kaibin Wan
    Jiamou Liu
    Yiwei Liu
    Zijian Zhang
    Bakhadyr Khoussainov
    Year: 2022
    Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure
    MOBICASE
    Springer
    DOI: 10.1007/978-3-030-99203-3_8
Kaibin Wan1, Jiamou Liu2, Yiwei Liu1, Zijian Zhang3,*, Bakhadyr Khoussainov4
  • 1: School of Computer Science and Technology
  • 2: School of Computer Science
  • 3: School of Cyberspace Science and Technology
  • 4: School of Computer Science and Engineering
*Contact email: zhangzijian@bit.edu.cn

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.

Keywords
Adversarial community detection Graph neural network Structural entropy
Published
2022-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99203-3_8
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