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数据分析与知识发现  0, Vol. Issue (): 1-     https://doi.org/10.11925/infotech.2096-3467. 2020.0531
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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
张思凡,牛振东,陆浩,朱一凡,王荣荣
(北京理工大学计算机学院 北京  100081)
(北京理工大学图书馆 北京  100081)
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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摘要 

[目的]提出一种文献被引量预测模型,用于发现潜在研究热点、促进刊物采编优化。

[方法]综合考虑了文献的关键词、作者、机构、国家、被引量等相关因素,利用图卷积进行特征提取,利用循环神经网络与注意力机制对被引量的时序信息与重要文献特征进行挖掘。

[结果]利用发表在WoS核心刊物上的交通运输领域的文献对模型进行了验证,在RMSE、MAE等各项指标上,与基准模型相比,最大提升幅度达到15.23%与16.91%。

[局限]在所提出的模型的预训练步骤中,进行了多次图卷积,使得算法的时间复杂度较高。

[结论]本文所提出的模型将文献各项特征充分融合,极大提高了预测模型的性能。

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关键词 被引量预测图卷积特征交叉     
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
     出版日期: 2020-10-09
ZTFLH:  TP393,G250  
引用本文:   
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-.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467. 2020.0531      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y0/V/I/1
[1] 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, 2020, 4(9): 56-67.
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