Knowledge Diffusion in Complex Networks

Abstract

Modern communication networks and social networks are the main tunnels of knowledge diffusion. Knowledge diffusion in complex networks is different from the epidemic like information spreading, since individuals are willing to learn and spread knowledge to their friends and the learning process can hardly be achieved in few conversations. In this paper, we investigate the important issue as which topological structure is suitable for knowledge diffusion. We propose a new knowledge diffusion model, where learning and forgetting mechanisms are considered. In this model, individuals can play imparter and learner simultaneously. Comparing the knowledge diffusion on a series of complex topologies, we observe that the individuals with a large degree can quickly learn more knowledge, who are beneficial to the knowledge diffusion. Our results surprisingly reveal that the networks with high degree heterogeneities are likely to be suitable for the knowledge diffusion. Our finding suggests that enhancing the degree heterogeneity of existing social networks may help to improve the performance of the knowledge diffusion. This result is well confirmed by our extensive simulation results. Our model therefore provides a theoretical framework for understanding the knowledge diffusion in complex topologies.