Abstract
Misinformation diffusion on network and its adverse effects are stimulus factors in designing efficient immunization strategies. We aim to study the node inoculation in the network which is exposed to nonlinear rumor propagation. In order to delimit the contagion on network the group based centrality is considered to order nodes according to their positional power and functional influence in the network. In the process of propagation dynamics, the strength of a node can be determined by the aspect of its connectivity to the other nodes in the network and the flow of contagion through edges depends on the strength of its two end nodes. Therefore, it is pertinent to study effect of immunization on network when misinformation propagation varies with tie strength between nodes. This paper considers degree dependent node strength which varies for every contact and determines nonlinear infectivity on the network. The competence of our proposed method can be established on empirical data sets which determines its adequacy to delimit rumor spread.
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Saxena, C., Doja, M.N., Ahmad, T. (2018). Group Based Immunization Strategy on Networks with Nonlinear Infectivity. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_14
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