Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system’s behaviour. In this paper we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.
Journal of Statistical Mechanics: Theory and Experiment
- Pub Date:
- August 2012
- Condensed Matter - Statistical Mechanics;
- Physics - Physics and Society;
- Quantitative Biology - Populations and Evolution
- 11 pages, 4 figures