Suppressed epidemics in multi-relational networks
arXiv:1409.4638 · doi:10.1103/PhysRevE.92.022812
Abstract
A two-state epidemic model in networks with links mimicking two kinds of relationships between connected nodes is introduced. Links of weights w1 and w0 occur with probabilities p and 1-p, respectively. The fraction of infected nodes rho(p) shows a non-monotonic behavior, with rho drops with p for small p and increases for large p. For small to moderate w1/w0 ratios, rho(p) exhibits a minimum that signifies an optimal suppression. For large w1/w0 ratios, the suppression leads to an absorbing phase consisting only of healthy nodes within a range p_L =< p =< p_R, and an active phase with mixed infected and healthy nodes for p < p_L and p>p_R. A mean field theory that ignores spatial correlation is shown to give qualitative agreement and capture all the key features. A physical picture that emphasizes the intricate interplay between infections via w0 links and within clusters formed by nodes carrying the w1 links is presented. The absorbing state at large w1/w0 ratios results when the clusters are big enough to disrupt the spread via w0 links and yet small enough to avoid an epidemic within the clusters. A theory that uses the possible local environments of a node as variables is formulated. The theory gives results in good agreement with simulation results, thereby showing the necessity of including longer spatial correlations.
9 pages, 6 figures
References in corpus (14)
- Statistical physics of social dynamics
- Multilayer Networks
- The structure and dynamics of multilayer networks
- Critical phenomena in complex networks
- Dynamical interplay between awareness and epidemic spreading in multiplex networks
- Diffusion dynamics on multiplex networks
- Multirelational Organization of Large-scale Social Networks in an Online World
- Structural measures for multiplex networks
- Thresholds for epidemic spreading in networks
- Emergence of network features from multiplexity
- Percolation in Multiplex Networks with Overlap
- Adaptive networks: coevolution of disease and topology
- Griffiths phases on complex networks
- Epidemic spreading on complex networks with general degree and weight distributions