Abstract:

This talk presents the introduction and our early investigation on coupled adaptive complex networks. Generally an adaptive network is the network whose topology adapts and evolves with the dynamics of the network. At present, adaptive networks are ubiquitous across many disciplines, including technical distribution networks such as road networks and the internet; natural and biological networks; and social science networks. These networks are often interact with or depend upon other networks, resulting in coupled adaptive networks.

In this talk, we present our recent study of susceptible-infected-susceptible (SIS) epidemic dynamics on coupled adaptive networks, where susceptible nodes are able to avoid contact with infection by rewiring their intra-network connections. However, infected nodes can pass the disease through inter-network connections, which do not change with time: the dependencies between the coupled networks remain constant.

An analytical formalism is developed and validated using extensive numerical simulation. The experiment results show that the stability is increased with the increase in the number of inter-network links, in the sense that the range of parameters over which both endemic and healthy states coexist (both solution branches are stable) becomes smaller. Also we find a new stable solution branch that does not appear in the case of single adaptive network but only in the case of weakly coupled networks, in which the disease is endemic in one network but neither becomes endemic nor dies out in the other. Instead, it persists only at the nodes that are coupled to nodes in the other network through inter-network links.