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Journal Articles Concurrency and Computation: Practice and Experience Year : 2017

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, because individuals are willing to learn and spread knowledge to their friends and the learning process can hardly be achieved in a few conversations. In this paper, we investigate the important issue as what topological structure is suitable for knowledge diffusion. We propose a new knowledge diffusion model, where both learning and forgetting mechanisms are considered. In this model, individuals can play imparter and learner simultaneously. Comparing 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 knowledge diffusion. Our results surprisingly reveal that the networks with high degree‐heterogeneity are likely to be suitable for knowledge diffusion. Our finding suggests that enhancing the degree heterogeneity of existing social networks may help to improve the performance of knowledge diffusion. This result is well confirmed by our extensive simulation results. Our model therefore provides a theoretical framework for understanding knowledge diffusion in complex topologies.
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Dates and versions

hal-02114935 , version 1 (30-04-2019)

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Yichao Zhang, Xing Li, M. Aziz-Alaoui, Cyrille Bertelle, Jihong Guan, et al.. Knowledge diffusion in complex networks. Concurrency and Computation: Practice and Experience, 2017, 29 (3), pp.e3791. ⟨10.1002/cpe.3791⟩. ⟨hal-02114935⟩
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