Attention-Based Multi-fusion Method for Citation Prediction

  • Juefei Wang
  • Fuquan Zhang
  • Yinan Li
  • Donglei LiuEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 156)


As the most common research style in the process of academic exchange, the paper plays a vital role in the specific links of knowledge communication, academic cooperation, and scientific research education. However, in the traditional field of bibliometrics, how to quantitatively evaluate the influence of a paper generally depends on the number of citation as a reference standard. The number of citation is an important indicator for evaluating papers, and it has serious problems of lag. Therefore, based on the relevant meta-information generated in the publication process of the literature, the prediction of the future influence of the literature can make up for the above defects. In order to accurately predict the future citations of the paper, this paper constructs the Attention Convolution Neural Network model and combines the bibliometrics and alternative metrology-related features to enrich the input vector. Experiments on data sets collected from WOS and ResearchGate show that the model has improved accuracy compared to traditional prediction models.


Attention convolution neural network Bibliometrics Altmetrics 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Juefei Wang
    • 1
  • Fuquan Zhang
    • 2
  • Yinan Li
    • 1
  • Donglei Liu
    • 1
    Email author
  1. 1.Computer SchoolBeijing Information Science and Technology UniversityBeijingChina
  2. 2.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina

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