The Ins and Outs of Network-Oriented Modeling: from Biological Networks and Mental Networks to Social Networks and Beyond

Network-Oriented Modeling is a relatively new way of modeling that is especially useful to model intensively interconnected and interactive processes. It has successfully been applied to model networks for a wide range of phenomena, including biological networks, networks of mental states, and social networks. In this lecture this modeling perspective will be discussed in more detail. It is discussed how the interpretation of a network as a causal network and taking into account dynamics brings more depth in the perspective. In the obtained notion of a temporal-causal network, nodes represent states with values that vary over time, and connections represent causal relations describing how states affect each other. As these causal relations themselves also may change, adaptive networks are covered as well. The wide scope of applicability of such a Network-Oriented Modelling approach will be analyzed in more depth and illustrated. This covers, for example, network models for principles of social contagion or information diffusion, and adaptive network models for principles of Hebbian learning in networks of mental states but also for principles of evolving social networks, such as the homophily principle and the triadic closure principle. From the methodological side, it will be discussed how mathematical analysis can be used to identify the relation between emergent dynamic properties concerning stabilizing or limit behaviour and network structure and settings. Finally, it will be discussed how requirements specification and verification can play an important role in the design process of a network model.