A unified approach to represent network adaptation principles by network reification

In this chapter, the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. This is explained for temporal-causal networks where connection weights, combination functions, and speed factors represent the characteristics for Connectivity, Aggregation, and Timing describing the network structure. Having the network structure represented in an explicit manner within the extended network enables to model the adaptation of the base network by dynamics within the reified network: an adaptive network is represented by a non-adaptive network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated for a number of well-known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.