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<p>Immunization strategies primarily aim to block the spreading process of viruses or information and to reduce the negative effects of these processes at a low cost. Previous immunization methods heavily rely on global network information to improve accuracy. However, in many cases, obtaining the global structure information of a network is impossible. Therefore, we propose a novel algorithm that relies on the local structural information of nodes and directly finds the bridge-hub nodes through the self-avoiding random walk algorithm. Compared with the acquaintance immunization method and the existing well-known related algorithms, our proposed algorithm has higher accuracy and stability and is less affected by infection probability. Furthermore, extensive experiments conducted with SIR epidemic model on real-world networks demonstrate that our algorithm can be widely used in the prevention and control of diseases and information in real networks with community structures.</p>

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An immunization strategy for community networks based on local structural information

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月13日

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  • Dayong Zhang,
  • Xuchen Meng,
  • Jiaye Sheng

摘要

Immunization strategies primarily aim to block the spreading process of viruses or information and to reduce the negative effects of these processes at a low cost. Previous immunization methods heavily rely on global network information to improve accuracy. However, in many cases, obtaining the global structure information of a network is impossible. Therefore, we propose a novel algorithm that relies on the local structural information of nodes and directly finds the bridge-hub nodes through the self-avoiding random walk algorithm. Compared with the acquaintance immunization method and the existing well-known related algorithms, our proposed algorithm has higher accuracy and stability and is less affected by infection probability. Furthermore, extensive experiments conducted with SIR epidemic model on real-world networks demonstrate that our algorithm can be widely used in the prevention and control of diseases and information in real networks with community structures.