Predicting citations based on graph convolution embedding and feature cross：case study of transportation research

[Objective] This paper proposes a citation prediction model for scholarly articles, which could identify potential research hot spots and optimize journal editing. [Methods] First, we used graph convolution to extract literature features, which include keywords, authors, institutions, countries, and citations. Then, we used recurrent neural network and attention model to examine the time-series information of citations and other features. [Results] We evaluated the proposed model with transportation articles from core journals indexed by the Web of Science. Compared with the benchmark model, our new method’s maximum improvements on RMSE and MAE were 15.23% and 16.91%. [Limitations] At the pre-training stage, our model adopted multiple graph convolutions, which was very time consuming. [Conclusions] The proposed model, which fully integrates literature features, could effectively predict their citations.