Co-evolution of structures and dynamics: network models and applications

Since complex networks are found ubiquitous across disciplines including social, biological and physical sciences, significant attention has focused on the implications of dynamics in establishing network structure and the impact of structural properties on dynamics on those networks. However, in many real systems the two processes are rarely isolated from each other, and the community increasingly realizes the importance of the interplay between the two. We examine a series of variants of a simple, abstract model for coevolution of a network and the opinions (i.e., states) of its members, arriving at one that captures many salient features of real networks, e.g., fragmentation and small-world effect. We then apply our machinery to the modeling of epidemic spread, to describe the rational behavior of people avoiding contacts with infected individuals. The co-evolution of dynamics not only leads to change in the social network but thus also influences the spread of disease. Our framework hence aims to provide more sophisticated tools and realistic predictions in the study of epidemics.