Immunization of networks with limited knowledge and temporary immunity

Modern view of network resilience and epidemic spreading has been shaped by percolation tools from statistical physics, where nodes edges are removed or immunized randomly a large-scale network. In this paper, we produce theoretical framework for studying targeted immunization in networks, only n can be observed at time with the most connected one among them being immunity it acquired may lost subject to decay probability ρ. We examine analytically properties as well scaling laws, which uncover distinctive characters Erdős–Rényi power-law networks two dimensions study both case fixed loss rate an asymptotic total scenario, paving way further understand temporary complex processes limited knowledge.