[Objective]This paper proposed a literature citation prediction model, which was used to discover potential research hotspots and promote the optimization of journal editing.
[Methods]Considering the relevant factors such as keywords, authors, institutions, countries, and citations of the literature, we used graph convolution to extract features, then used recurrent neural network and attention model to mine the time-series information of citations and important literature features.
[Results]Using transportation related literature published in WoS core journals, compared with the benchmark model, the model has a maximum improvement of 15.23% and 16.91% on RMSE, MAE and other indicators.
[Limitations]In the pre-training step of the proposed model, multiple graph convolutions were performed, which made the time complexity high.
[Conclusions] The model proposed in this paper fully integrated the features of the literature, then greatly improved the performance of the citation prediction model.
张思凡, 牛振东, 陆浩, 朱一凡, 王荣荣. 基于图卷积嵌入与特征交叉的文献被引量预测方法：以交通运输领域为例
[J]. 数据分析与知识发现, 0, (): 1-.
Sifan Zhang, Zhendong Niu, Hao Lu, Yifan Zhu, Rongrong Wang. Graph Convolution Embedding and Feature Cross Based Literature Citation Prediction Method：Taking the Transportation Field as An Example
. Data Analysis and Knowledge Discovery, 0, (): 1-.