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With the rapid advancement of information technology, social networks have become indispensable tools in people’s lives, connecting a vast number of internet users. While community detection provides convenience to users by uncovering potential community structures within social networks, it also raises privacy concerns. This paper explores several algorithms aimed at protecting community structure privacy through modifications to network topology. It presents a theoretical proof of the edge-adding principle in modularity minimization algorithms, optimizes the efficiency of the Residual Entropy Minimization (REM) model, and examines the effectiveness of the REM algorithm in safeguarding community privacy across different community structures in generated graphs.

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Further Exploration of the REM Model on Community Structure Deception

  • 会议论文

  • 发表日期：

    2026年

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  • Yiwei Liu,
  • Guoyuan Li,
  • Peng Yin,
  • Situ Ma,
  • Yizheng Ge,
  • Lin Zhang,
  • Jialing He

摘要

With the rapid advancement of information technology, social networks have become indispensable tools in people’s lives, connecting a vast number of internet users. While community detection provides convenience to users by uncovering potential community structures within social networks, it also raises privacy concerns. This paper explores several algorithms aimed at protecting community structure privacy through modifications to network topology. It presents a theoretical proof of the edge-adding principle in modularity minimization algorithms, optimizes the efficiency of the Residual Entropy Minimization (REM) model, and examines the effectiveness of the REM algorithm in safeguarding community privacy across different community structures in generated graphs.