 Complex networks have been used to represent the fundamental structure of a multitude of complex systems from various fields. In the network representation, the system is reduced to a set of nodes and links that denote the elements of the system and the connections between them respectively. Complex networks are commonly adaptive such that the structure of the network and the states of nodes evolve dynamically in a coupled fashion. Adaptive networks lead to peculiar complex dynamics and network topologies, which can be investigated by momentclosure approximations, a coarsegraining approach that enables the use of the dynamical systems theory. In this thesis, I study several contact processes in adaptive networks that are defined by the transmission of node states. Employing momentclosure approximations, I establish analytical insights into complex phenomena emerging in these systems. I provide a detailed analysis of existing alternative momentclosure approximation schemes and extend them in several directions. Most importantly, I consider developing analytical approaches for models with complex update rules and networks with complex topologies.
I discuss four dierent contact processes in adaptive networks. First, I explore the effect of cyclic dominance in opinion formation. For this, I propose an adaptive network model: the adaptive rockpaperscissors game. The model displays four different dynamical phases (stationary, oscillatory, consensus, and fragmented) with distinct topological and dynamical properties. I use a simple momentclosure approximation to explain the transitions between these phases.
Second, I use the adaptive voter model of opinion formation as a benchmark model to test and compare the performances of major momentclosure approximation schemes in the literature. I provide an indepth analysis that leads to a heightened understanding of the capabilities of alternative approaches. I demonstrate that, even for the simple adaptive voter model, highly sophisticated approximations can fail due to special dynamic correlations. As a general strategy for targeting such problematic cases, I identify and illustrate the design of new approximation schemes specific to the complex phenomena under investigation.
Third, I study the collective motion in mobile animal groups, using the conceptual frame work of adaptive networks of opinion formation. I focus on the role of information in consensus decisionmaking in populations consisting of individuals that have conflicting interests. Employing a momentclosure approximation, I predict that uninformed individuals promote democratic consensus in the population, i.e. the collective decision is made according to plurality. This prediction is conrmed in a fish school experiment, constituting the rst example of direct verification for the predictions of adaptive network models.
Fourth, I consider a challenging problem for momentclosure approximations: growing adaptive networks with strongly heterogeneous degree distributions. In order to capture the dynamics of such networks, I develop a new approximation scheme, from which analytical results can be obtained by a special coarsegraining procedure. I apply this analytical approach to an epidemics problem, the spreading of a fatal disease on a growing population. I show that, although the degree distribution has a finite variance at any nite infectiousness, the model lacks an epidemic threshold, which is a genuine adaptive network effect. Diseases with very low infectiousness can thus persist and prevail in growing populations.
