Complex networks, such as the Internet and sexual partnership networks, often have scale-free properties, meaning their nodes have a power-law distribution of connections. This structure makes them highly vulnerable to epidemic outbreaks. Uniform immunization strategies, where individuals are randomly immunized, are ineffective in scale-free networks because they lack a critical immunization threshold. In contrast, targeted immunization strategies, which focus on highly connected nodes, significantly reduce network vulnerability to epidemics. In scale-free networks, the presence of a few highly connected nodes (hubs) allows diseases to spread more easily. Targeted immunization, which immunizes these hubs, drastically lowers the prevalence of infections. This is because the hubs are the primary drivers of disease transmission. In contrast, uniform immunization does not effectively reduce the spread of infections in scale-free networks, even with high immunization rates. The study compares two network models: the Watts-Strogatz (WS) model, which has small-world properties and homogeneous connectivity, and the Barabási-Albert (BA) model, a scale-free network. The SIS model, which describes the spread of infections in a population, is used to analyze the effectiveness of different immunization strategies. In WS networks, uniform immunization can effectively reduce infection prevalence, but in BA networks, it is not sufficient. Targeted immunization, however, is highly effective in BA networks. The results show that scale-free networks require targeted immunization strategies to prevent major outbreaks. This is because the heterogeneity in connectivity in scale-free networks means that a small fraction of highly connected nodes can significantly reduce the spread of infections. The study also highlights the importance of considering network structure when designing immunization strategies, as uniform strategies are not effective in scale-free networks. The findings have practical implications for public health and network security. For example, in the context of the Internet, targeted immunization of high-traffic nodes can prevent the spread of digital epidemics. Similarly, in human sexual networks, targeted immunization of highly connected individuals can help control the spread of sexually transmitted diseases. The study emphasizes the need for tailored immunization strategies that take into account the specific structure of the network.Complex networks, such as the Internet and sexual partnership networks, often have scale-free properties, meaning their nodes have a power-law distribution of connections. This structure makes them highly vulnerable to epidemic outbreaks. Uniform immunization strategies, where individuals are randomly immunized, are ineffective in scale-free networks because they lack a critical immunization threshold. In contrast, targeted immunization strategies, which focus on highly connected nodes, significantly reduce network vulnerability to epidemics. In scale-free networks, the presence of a few highly connected nodes (hubs) allows diseases to spread more easily. Targeted immunization, which immunizes these hubs, drastically lowers the prevalence of infections. This is because the hubs are the primary drivers of disease transmission. In contrast, uniform immunization does not effectively reduce the spread of infections in scale-free networks, even with high immunization rates. The study compares two network models: the Watts-Strogatz (WS) model, which has small-world properties and homogeneous connectivity, and the Barabási-Albert (BA) model, a scale-free network. The SIS model, which describes the spread of infections in a population, is used to analyze the effectiveness of different immunization strategies. In WS networks, uniform immunization can effectively reduce infection prevalence, but in BA networks, it is not sufficient. Targeted immunization, however, is highly effective in BA networks. The results show that scale-free networks require targeted immunization strategies to prevent major outbreaks. This is because the heterogeneity in connectivity in scale-free networks means that a small fraction of highly connected nodes can significantly reduce the spread of infections. The study also highlights the importance of considering network structure when designing immunization strategies, as uniform strategies are not effective in scale-free networks. The findings have practical implications for public health and network security. For example, in the context of the Internet, targeted immunization of high-traffic nodes can prevent the spread of digital epidemics. Similarly, in human sexual networks, targeted immunization of highly connected individuals can help control the spread of sexually transmitted diseases. The study emphasizes the need for tailored immunization strategies that take into account the specific structure of the network.