Academic team influence propagation prediction method and device, and storage medium

Abstract

The invention discloses an academic team influence propagation prediction method and a device, and a storage medium. Internal organization structure characteristics and external propagation characteristics of the team are considered; a community node vector in a cooperative relationship network with an author cooperation relationship is combined with a propagation node vector in an influence propagation directed network with an author reference relationship; a deep full-connection neural network link prediction model is adopted to realize propagation prediction of team influence; according tothe method, the deep learning model is used for solving the team influence propagation link prediction problem in the complex network, the universality is good, and the limitation of link prediction of a traditional method and the defect that an existing deep learning method is difficult to predict the team influence propagation direction are overcome. Experiments of a real data set show that themethod has high prediction accuracy.

Description

technical field [0001] The present invention relates to the technical field of forecasting the spread of individual influence in a group, in particular to a method, device and storage medium for predicting the spread of influence of an academic team. Background technique [0002] Using complex network theory and methods to study the influence and communication behavior of individuals and groups is of great significance in the application fields of social e-commerce, public opinion analysis, and influence analysis in the information age. In the field of academic research, scholars publish their research results in academic journals, which is an important way to spread academic ideas. The influence of an excellent academic team will play a leading role in the development of scientific research. Predicting the spread of the influence of an academic team is a hot research issue in complex network analysis and can be used for the selection and evaluation of scientific and technol...

AI Technical Summary

Problems solved by technology

In order to achieve the purpose of learning hidden deep features from the original global features of the network, Perozzi et al. proposed DeepWalk in 2014. [1] Algorithm, the algorithm represents the low-dimensional vector representation of the node walk sequence through random walk in the network, and obtains the node representation with the global structural characteristics of the network. It is the basic method of current network representation learning, but this method can only represent The propagation situation between two nodes in the network is also unable to predict the propagation situation of team influence

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