Search

Recent years have witnessed a surge of research on the identification of key users in online communities. However, seldom research has focused on their knowledge diffusion capabilities. The purpose of this study is to propose a new measure to find key users who perform well in knowledge diffusion in order to promote users' active participation in Q&A communities. In particular, this article develops an improved measure consolidating both users' structural hole and knowledge diffusion capability and evaluates its performance through a field study involving 230,000 users and more than 132 million network relations of users. Our results show that our proposed measure can be used to detect key users who occupy structural holes' advantages in social networks. In addition, key users detected by our proposed measure generally perform well on nearly all dimensions of knowledge diffusion capability compared with other measures of key users. Our study entails important theoretical and practical contributions. [ABSTRACT FROM AUTHOR]

Based on the fact that one can attain knowledge by oneself, which is different from epidemic spreading, we analyze the knowledge transmission in complex networks. In this paper, we propose a knowledge transmission model by considering the self-learning mechanism and derive the mean-field equations that describe the dynamics of the knowledge transmission process. Furthermore, we obtain the transmission threshold R 0 , which is closely related with the transmission rate and self-learning rate. Moreover, we investigate the global stability of the knowledge free equilibrium E 0 and the endemic equilibrium E * of the model. That is, when R 0 < 1, the knowledge free equilibrium point E 0 is globally asymptotically stable and the knowledge becomes completely extinct eventually; when R 0 > 1, a unique endemic equilibrium point E * is globally stable, and the knowledge can be transmitted. Finally, numerical simulations are given to illustrate the theoretical results. The simulation results indicate that the self-learning factor has an obvious promoting effect on the knowledge transmission, both in scale-free and homogeneous networks. Besides, the simulation results illustrate that the scale-free network is more efficient to knowledge transmission. [ABSTRACT FROM AUTHOR]