Networks are inherently complex dynamical systems, where both attributes of individuals (nodes) and topology of the network (links) can have inter-coupled dynamics. For instance, it is known that social networks tend to divide into groups, or communities, of like-minded individuals. One can ask whether individuals become likeminded because they are connected via the network, or whether they form network connections
To model the interplay between influence and selction, we have developed a computational models of co-evolving networks based on interacting Hidden Markov Processes. The idea behind this approach is that he network is shaped by the interaction of local dynamical processes unfolding on individual nodes, while those processes themselves are influenced by the changing network structure. This provides a feedback mechanism that is vital for capturing realistic behavior of complex real-world network.
Another model of co-evolving dynamics is based on agents involved in game theoretical interactions. Specifically, we consider network-augmented multi-agent systems where, at each time step, agents choose which neighbor to play with, and which strategy to choose. As agents play repeatedly with each other, they will adapt their behavior by reinforcing (penalizing) both links and strategies that provide good (bad) outcomes. Thus, the reinforcment affects both the strategy and network