Structural Evolution in knowledge transfer network: An agent-based model

Abstract

We use an agent-based model to study the effect of knowledge transfer on the structural evolution of a social network. In the proposed model, the agents exchange knowledge with their network neighbors; and simultaneously they adjust their neighbors by edge-rewiring in order seek better chance for knowledge transfer. This gives rise to the coevolution of the population's knowledge state and the network topology. Through computational simulations, interesting phenomena are observed, most notably the disassembly and reassembly of the network connectivity and the emergence of the small-world structure that is self-organized from the initial random network. The underlying mechanisms are partly analyzed. © 2013 Springer-Verlag Berlin Heidelberg.