An effective immunization strategy in social networks

Diffusion processes are an efficient tool to unfold the structural characteristics of social networks. One of the challenges, which engaged researcher have faced, is immunizing the networks against virus dissemination. In this paper, we propose an efficient immunization algorithm against the epidemic diffusion model. Since the largest eigenvalue of the matrix of graphs is associated with the connectivity, the aim of the efficiency is decreasing this indicator significantly after immunization process in which virus spreading would also be ceased dramatically. Noteworthy outcomes we would like to highlight are: a) the proposed algorithm corresponds to the whole network structure rather than local information of nodes, and b) our algorithm performs better in networks with high clustering coefficient and transitivity.