RESUMO
Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.
RESUMO
In this article, the phenomenon of scientist cooperation in the scientist cooperation network is studied from the perspectives of information spread and link prediction. By mining the information in the scientist cooperation network, analyzing the cooperation has been generated and discovering potential cooperation opportunities. It helps to build a richer cooperation network with more content. Information spread can reflect the inner laws of network structure formation, and the link prediction method can retain the integrity of network information to the maximum extent. First, the real network is abstracted by analyzing its structure as well as node attributes into a simulated network. Second, the process of information spread in the cooperation network is simulated by improving the traditional SIS model. Some improvements are made to the link prediction algorithm for the impact brought to the network by information spread. Finally, the experimental results in the scientist cooperation network show that the hybrid weighted link prediction algorithm combining node attributes and spread factors can improve the accuracy of link prediction and provide suggestions for scientists to find partners. The comparative experiments on simulated and real networks not only validate the effectiveness of the propagation model in the scientist cooperation network, but also verify the accuracy of the hybrid weighted link prediction algorithm.
