An immunization strategy for community networks based on local structural information

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.