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数据分析与知识发现  2020, Vol. 4 Issue (9): 56-67     https://doi.org/10.11925/infotech.2096-3467.2020.0531
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
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1,牛振东1,2(),陆浩1,朱一凡1,王荣荣1
1北京理工大学计算机学院 北京 100081
2北京理工大学图书馆 北京 100081
Predicting Citations Based on Graph Convolution Embedding and Feature Cross:Case Study of Transportation Research
Zhang Sifan1,Niu Zhendong1,2(),Lu Hao1,Zhu Yifan1,Wang Rongrong1
1School of Computer, Beijing Institute of Technology, Beijing 100081, China
2Beijing Institute of Technology Library, 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、优化改进刊物采编工作。【方法】 综合考虑文献的关键词、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Web of Science核心集中交通运输领域的文献对模型进行验证,与基准模型相比,在RMSE、MAE等各项指标上最大提升幅度达15.23%与16.91%。【局限】 在所提模型的预训练步骤中,进行多次图卷积,使得算法的时间复杂度较高。【结论】 本文所提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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张思凡
牛振东
陆浩
朱一凡
王荣荣
关键词 被引量预测图卷积特征交叉    
Abstract

[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.

Key wordsCitation Prediction    Graph Convolution    Feature Cross
收稿日期: 2020-06-08      出版日期: 2020-10-14
ZTFLH:  TP393  
基金资助:*本文系国家重点研发计划基金项目“专业内容知识聚合服务技术研发与创新服务示范”的研究成果之一(2019YFB1406302)
通讯作者: 牛振东     E-mail: zniu@bit.edu.cn
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, 2020, 4(9): 56-67.
Zhang Sifan,Niu Zhendong,Lu Hao,Zhu Yifan,Wang Rongrong. Predicting Citations Based on Graph Convolution Embedding and Feature Cross:Case Study of Transportation Research. Data Analysis and Knowledge Discovery, 2020, 4(9): 56-67.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0531      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I9/56
Fig.1  关键词与作者共现关系数据
分类 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
K 769 870 917 1 009 1 088 624 703 528 349 453 325
A 646 677 832 965 1 040 1 144 1 314 561 731 740 777
I 1 382 1 540 1 616 1 768 1 711 714 776 877 474 422 355
C 66 62 59 59 65 68 69 75 76 80 79
Table 1  关键词、作者、机构、国家网络中的节点数量
数据 标签
2008年-2012年 K/A/I/C/count 2013年 count
2009年-2013年 K/A/I/C/count 2014年 count
2010年-2014年 K/A/I/C/count 2015年 count
2011年-2015年 K/A/I/C/count 2016年 count
2012年-2016年 K/A/I/C/count 2017年 count
Table 2  被引量预测数据与标签
Fig.2  预测模型整体架构
模型 RMSE MAE R-Squared
AVR 326.27 340.40 0.731 5
GMM 293.25 312.73 0.805 3
NNCP 279.42 282.36 0.851 5
本文模型 276.58 282.73 0.867 9
Table 3  不同模型实验结果对比
Fig.3  各项评估指标随向量维度的变化
模型修改 RMSE MAE R-Squared
去除交叉网络 325.74 340.25 0.750 9
替换GRU层 307.24 320.54 0.816 3
去除注意力层 289.53 302.76 0.842 4
本文方法 276.58 282.73 0.867 9
Table 4  不同模块作用对比
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