Self-organizing techniques for knowledge diffusion in dynamic social networks

In this paper, we model a knowledge diffusion process in a dynamic social network and study two different techniques for self-organization aimed at improving the average knowledge owned by agents and the overall knowledge diffusion within the network. One is a weak self-organization technique requiring a system-level central control, while the other is a strong self-organization technique that each agent exploits based on local information only. The two techniques are aimed at increasing the knowledge diffusion by mitigating the hype effect and the network congestion that the system dynamics shows systematically. Results of simulations are analyzed for different configurations, discussing how the improvements in knowledge diffusion are influenced by the emergent network topology and the dynamics produced by interacting agents. Our theoretical results, while preliminary, may have practical implications in contexts where the polarization of interests in a community is critical.