Knowledge diffusion in complex networks

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.

现代通信网络和社交网络是知识传播的主要渠道。复杂网络中的知识传播不同于类似流行病的信息传播，因为个体愿意向朋友学习和传播知识，且学习过程很难通过几次交谈就完成。在本文中，我们研究了一个重要问题：什么样的拓扑结构适合知识传播。我们提出了一种新的知识传播模型，该模型同时考虑了学习和遗忘机制。在这个模型中，个体可以同时扮演知识传授者和学习者的角色。通过比较一系列复杂拓扑结构上的知识传播，我们发现度较大的个体能够更快地学习到更多知识，这对知识传播是有益的。我们的研究结果令人惊讶地表明，高度异质性的网络可能适合知识传播。我们的发现表明，增强现有社交网络的度异质性可能有助于提高知识传播的性能。我们大量的模拟结果很好地证实了这一结果。因此，我们的模型为理解复杂拓扑结构中的知识传播提供了一个理论框架。