In this brief, we study epidemic spreading dynamics taking place in complex networks. We specifically investigate the effect of synergy, where multiple interactions between nodes result in a combined effect larger than the simple sum of their separate effects. In this context, we derive an upper bound on the growth rate of the synergistic Susceptible-Infected-Susceptible model in terms of the eigenvalues of a matrix whose size grows quadratically with the number of the nodes in the network. In our derivation, we neither limit our focus on a specific class of complex networks such as lattices, nor use mean-field approximation techniques, as were often done in the literature. The effectiveness of our theoretical result is numerically illustrated by simulations on empirical human and animal social networks.
In this brief, we study epidemic spreading dynamics taking place in complex networks. We specifically investigate the effect of synergy, where multiple interactions between nodes result in a combined effect larger than the simple sum of their separate effects. Although synergistic effects play key roles in various biological and social phenomena, their analyses have been often performed by means of approximation techniques and for limited types of networks. In order to address this limitation, this paper proposes a rigorous approach to quantitatively understand the effect of synergy in the Susceptible-Infected-Susceptible model taking place in an arbitrary complex network. We derive an upper bound on the growth rate of the synergistic Susceptible-Infected-Susceptible model in terms of the eigenvalues of a matrix whose size grows quadratically with the number of the nodes in the network. We confirm the effectiveness of our result by numerical simulations on empirically observed human and animal social networks.
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In this context, we
Although synergistic effects play key roles in various biological and social phenomena, their analyses have been often performed by means of approximation techniques and for limited types of networks. In order to address this limitation, this paper proposes a rigorous approach to quantitatively understand the effect of synergy in the Susceptible-Infected-Susceptible model taking place in an arbitrary complex network. We
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In our derivation, we neither limit our focus on a specific class of complex networks such as lattices, nor use mean-field approximation techniques, as were often done in the literature. The
We confirm the
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theoretical result is numerically illustrated by simulations on empirical
result by numerical simulations on empirically observed