Adaptive Coevolutionary Networks

• Thilo Gross, Max-Planck Institute for Physics of Complex Systems, Dresden, Germany

Adaptive networks (also called coevolutionary networks) are a class of dynamical and dynamically evolving networks. The defining feature of adaptive networks is the presence of a feedback loop between a dynamical process taking place on the network and a dynamically evolvig network structure. The interplay between these two types of dynamics can lead to a rich interplay that is thought to drive the dynamics and development of many real world networks.

Every network is a system of discrete nodes connected by links. Applied to such systems the word dynamic can have two different meanings. First, a network can be dynamic because the network topology (the specific structure of nodes and links) evolves in time. In this sense, for instance every growing network such as the Barabasi-Albert Model is dynamic. Second, a network can be dynamic because there is a dynamical process taking place on the network. Well-studied examples include for instance models of disease spreading or opinion formation on static networks. We can thus distinguish between the dynamics of networks and dynamic on networks. Adaptive networks combine these two types of dynamics. In particular one talks of adaptive networks when the dynamics of and on the networks interact. Such an interplay is found in a wide range of real world systems and gives rise to a range of phenomena that are not observed either dynamics on networks or dynamics of networks in isolation.

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Sponsored by: Leo Trottier, Department of Cognitive Science, University of California, San Diego, CA, USA