2019/02/08 by Masaki Ogura, Ogura, Masaki, Wenjie Mei +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #FOS: Biological sciences #FOS: Computer and information sciences #Populations and Evolution (q-bio.PE) #Social and Information Networks (cs.SI) #cs.SI #q-bio.PE
paper · pdf · doi:10.48550/arxiv.1902.02954
Accepted for publication in IEEE Transactions on Circuits and Systems II: Express Briefs
arxiv created 2019/04/19 · arxiv updated 2019/04/23
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.