In this work, we  study stability and robustness to noise in deterministic SIS epidemics over large networks by using the spectral properties of graphons . We consider the presence of additive noise in a deterministic SIS model over a random graph sampled from a piecewise Lipschitz graphon with finite rank. We derive a noise index to evaluate the deviation from the disease-free state due to noise. For finite graphs, the index depends on the adjacency eigenvalues of the graphs. Using the eigenvalues of the associated graphon operator, we are able to find an approximation of the index that is tight when the network size goes to infinity. A numerical example is included to illustrate the results.
In this work, we use the spectral properties of graphons to study stability and sensitivity to noise of deterministic SIS epidemics over large networks . We consider the presence of additive noise in a linearized SIS model and we derive a noise index to quantify the deviation from the disease-free state due to noise. For finite networks, we show that the index depends on the adjacency eigenvalues of its graph. We then assume that the graph is a random sample from a piecewise Lipschitz graphon with finite rank and, using the eigenvalues of the associated graphon operator, we find an approximation of the index that is tight when the network size goes to infinity. A numerical example is included to illustrate the results.
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deterministic SIS model over a random graph sampled from a piecewise Lipschitz graphon with finite rank. We
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