【摘要】 动态异质性网络上的知识及信息流动的研究是当前的热点问题.为了分析动态异质性知识网络上的多种知识积累过程,本文建立了对应的网络模型和知识积累模型.知识网络动态变化策略分为两种:一是网络均衡策略,即在知识网络演化过程中,主体之间的平均联系强度和联系数量保持不变;二是网络增长变化策略,即在网络演化过程中知识主体之间的联系逐渐增加.各种知识的积累量分为两部分:一是以S型增长曲线为特征的主体自身的知识学习,二是通过知识关联度矩阵计算相邻知识主体之间的知识流动量.仿真结果表明,知识关联度较高的知识在网络增长策略下的流动速度快于知识关联度较低知识,知识流动速度快的网络演化策略下的知识主体之间的知识量方差小于知识流动速度较慢的网络演化策略.更多还原

【Abstract】 Structural changes on the complex network and the resulting knowledge and information flow variation are hot issues of current study. To analyze the accumulation process of the different knowledge on the heterogeneous complex network,the corresponding network model as well knowledge accumulation model is formulated in this paper. The change strategies of the heterogeneous knowledge network are divided into two categories. The first is equilibrium strategy,according to which the average account and the strength of connecting links between agents on the knowledge network remain fixed during the network evolution process. The second is the network growth strategy,which refers to that the connecting links between agents of the knowledge network are gradually increased. Correspondingly,the cumulative amount of all kinds of knowledge is categorized into two parts. The first part is the self-learning knowledge that is characterized by an S-shaped growth curve.The second is the external knowledge calculated according to the correlation matrix and the neighbor relationships. Simulation results show that a higher correlation degree of knowledge gets a faster flow speed in the network’s growth strategy and there is no evidence in the network’s balance strategy. Moreover,the variance of knowledge in the network’s growth strategy is smaller than that in the network’s balance strategy.更多还原