基于多图卷积神经网络和注意力机制的学术新星预测方法.

Identifying potential academic rising stars from academic newcomers can provide decision support for tasks such as talent introduction, project review, and expert database construction, which has important research significance and application value and has received extensive attention from the academic community. However, the existing academic rising star prediction methods do not organically combine the academic cooperation relationship and individual attribute information, resulting in low accuracy. To solve the above problem, this paper proposes an academic rising star prediction method MGCNA based on multi-graph convolutional neural network and attention mechanism. It comprehensively considers cooperative networks and similar networks. Based on the two networks, the graph convolutional neural network is used to learn the author􀆳s feature representation, and then the attention mechanism is used for information fusion, so as to predict the academic rising stars with high potential. Finally, experiments are carried out on real datasets from the ArnetMiner platform, and the experimental results demonstrate the effectiveness of MGCNA in predicting academic rsing star tasks. [ABSTRACT FROM AUTHOR]