基于交互频率的动态网络上的社会知识传播研究

Social Knowledge Diffusion on Dynamical Networks in Terms of Interaction Frequency

知识传播过程和社会网络结构的演化往往是同步进行的.基于交互频率的动态网络社会知识传播模型(SKD)在知识传播过程中,随机选取的目标节点会依据与邻居节点的交互频率来决定知识传播的对象,或者断边重连到网络中的任意一个非邻居节点.将SKD模型与随机化模型和基于知识距离的传统知识传播模型(TKD)做了对比实验,实验结果表明:SKD模型的知识传播速度要快于随机化模型和TKD模型;更重要的是,SKD模型在网络结构演化过程中呈现出同配性,网络结构的同配性是社交网络的一项基本结构属性.该工作对于理解知识传播和网络结构的联合演化过程具有十分重要的意义.

The knowledge diffusion process and social network structure are always evolving simutaneously. By taking into account the interaction frequency which is always used to measure the social closeness, the social knowledge diffusion （SKD） model for dynamic networks was presented. In the model, with probability p, the target node would preferentially select one neighbor node to transfer knowledge according to their interaction frequency instead of the knowledge distance. Otherwise, with probability 1- p, the target node would build a new link with one node in the system randomly. The simulation results show that, comparing with the random model defined by the random selection mechanism and the traditional knowledge diffusion （TKD） model driven by knowledge distance, the knowledge will spread more fast and more importantly, the network structure leads to an assortative one, which is a fundamental feature of social networks. The work is helpful for deeply understanding the coevolution of the knowledge diffusion and network structure.