计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (9): 376-387.doi: 10.11896/jsjkx.240800107

• 信息安全 • 上一篇    下一篇

基于梯度引导的社团隐匿扰动子结构优化方法

俞山青, 宋亦聃, 周金涛, 周梦, 李家祥, 汪泽钰, 宣琦   

  1. 浙江工业大学网络空间安全研究院 杭州 310023
    杭州市滨江区浙工大人工智能创新研究院 杭州 310056
  • 收稿日期:2024-08-21 修回日期:2024-11-11 出版日期:2025-09-15 发布日期:2025-09-11
  • 通讯作者: 汪泽钰(vencent_wang@outlook.com)
  • 作者简介:(yushanqing@zjut.edu.cn)
  • 基金资助:
    国家自然科学基金(62103374,U21B2001);浙江省重点研发计划(2022C01018,2024C01025)

Gradient-guided Pertuerbed Substructure Optimization for Community Hiding

YU Shanqing, SONG Yidan, ZHOU Jintao, ZHOU Meng, LI Jiaxiang, WANG Zeyu, XUAN Qi   

  1. Institute of Cyberspace Security,Zhejiang University of Technology,Hangzhou 310023,China
    Binjiang Institute of Artificial Intelligence,Zhejiang University of Technology,Hangzhou 310056,China
  • Received:2024-08-21 Revised:2024-11-11 Online:2025-09-15 Published:2025-09-11
  • About author:YU Shanqing,born in 1983,Ph.D,associate professor,Ph.D supervisor,is a member of CCF(No.C3006M).Her main research interests include intelligent computation and data mining.
    WANG Zeyu,born in 1999,postgra-duate.His main research interests inclu-de data mining and network security.
  • Supported by:
    National Natural Science Foundation of China(62103374,U21B2001) and Key R&D Program of Zhejiang Province(2022C01018,2024C01025).

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摘要： 社团检测是一种用于揭示网络聚集行为的技术,能够精准识别网络中的社团结构,帮助更好地理解复杂网络的内部组织和功能。然而,随着社团检测算法的快速发展,其中信息泄露和过度挖掘等诸多隐私问题也备受关注。因此,社团隐匿算法被广泛研究,它通过构建扰动子结构来模糊网络中的社团结构,从而有效地降低社团检测算法的识别能力,实现隐私保护。在现有的扰动子结构优化方法中,基于遗传算法的方法表现较为突出,但这些方法在搜索解过程中缺少方向性指导,因此在构建扰动子结构的效果和效率上仍有提升空间。通过将梯度引导信息引入遗传算法搜索,可以优化扰动子结构的构建过程,从而提高社团隐匿的效果和效率。实验结果表明,在社团隐匿问题中加入梯度引导信息的遗传算法,在搜索扰动子结构方面显著优于其他基线方法,证明了其有效性。

关键词: 社团检测, 社团隐匿, 梯度优化, 进化计算, 扰动子结构

Abstract: Community detection is a technique used to reveal network clustering behaviors,capable of accurately identifying the community structure within a network,thus helping to better understand the internal organization and functions of complex networks.However,with the rapid development of these algorithms,concerns have arisen regarding issues such as information leakage and privacy invasion.In response,community hiding algorithms have been widely studied,which reduce the effectiveness of community detection algorithm and realize privacy protection by constructing perturbed substructure to blur the community structure in the network.Among the current methods for optimizing perturbation substructures,genetic algorithm-based approaches performs better.However,these methods often lack guidance directional in the search for solutions,leaving room for improvement in both the effectiveness and efficiency of constructing perturbation substructures.By incorporating gradient-guided information into the genetic algorithm search,the construction process of perturbation substructures can be optimized,enhancing the effectiveness and efficiency of community hiding.Experimental results demonstrate that integrating gradient-guided information into genetic algorithm search for perturbation substructures significantly outperforms other baseline methods for community hiding,proving its effectiveness.

Key words: Community detection, Community hiding, Gradient optimization, Evolutionary algorithm, Perturbed substructure

中图分类号: 

  • TP302
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