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Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2096-3467. 2020.0531
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Graph Convolution Embedding and Feature Cross Based Literature Citation Prediction Method:Taking the Transportation Field as An Example
Sifan Zhang,Zhendong Niu,Hao Lu,Yifan Zhu,Rongrong Wang
(School of Computer, Beijing Institute of Technology, Beijing 100081, China)
(Library, Beijing Institute of Technology, Beijing 100081, China)
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Abstract  

[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.

Key words Citation Prediction      Graph Convolution      Feature Cross      
Published: 09 October 2020
ZTFLH:  TP393,G250  

Cite this article:

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-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467. 2020.0531     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

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