Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks

Abstract EN

Epidemic models trade the modeling accuracy for complexity reduction.

This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy.

A group-based continuous-time Markov SIS model is developed.

The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model.

By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models.

An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix.

The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale.

Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.