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数据分析与知识发现  2018, Vol. 2 Issue (11): 73-79     https://doi.org/10.11925/infotech.2096-3467.2018.0254
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
新能源领域专利转让加权网络中主体间技术交易机会预测*
武玉英, 孙平(), 何喜军, 蒋国瑞
北京工业大学经济与管理学院 北京 100124
北京现代制造业发展研究基地 北京 100124
Predicting Transactions Among Agents in Patent Transfer Weighted Networks for New Energy
Wu Yuying(), Sun Ping, He Xijun, Jiang Guorui
College of Economics and Management, Beijing University of Technology, Beijing 100124, China
Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China
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摘要 

【目的】通过挖掘专利转让加权网络主体信息及结构特征对交易机会进行预测, 促进技术供需有效对接。【方法】采集新能源领域2012年-2016年数据构建专利转让加权网络, 通过熵权法融合网络结构与内容指标, 结合网络真实权重与结构权重, 利用BP神经网络预测技术交易机会及其权重。【结果】融合结构指标RA与内容指标Cosine的预测精度达到94.28%, 在所有指标组合中最高; 结合网络真实权重与结构权重预测链接权重, 预测误差有所降低。【局限】模型未充分考虑节点属性及网络演化机制。【结论】链路预测方法具有较高预测精度, 能更全面挖掘专利技术交易网络中潜在供需主体对及其权重, 对实践具有一定指导意义。

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武玉英
孙平
何喜军
蒋国瑞
关键词 交易机会预测加权网络结构相似性内容相似性BP神经网络    
Abstract

[Objective] This paper examines the structure of weighted network for patent transfers as well as the characteristics of agents, aiming to predict transaction opportunities and promote the connection of technology supply and demand. [Methods] First, we constructed a weighted network for patented technology transactions based on data from 2012 to 2016. Then, we used the entropy method to combine its structure and contents. Finally, we used the BP neural network to predict transaction opportunities and weights. [Results] The prediction accuracy by the proposed method, which combined the structure index RA and the content index Cosine, was the highest. The prediction error was also reduced by using the real and structure weights of the network to predict the link weight. [Limitations] More research is needed to study the Node properties and network evolution mechanism. [Conclusions] The link prediction method has a higher precision, which help us find potential supply and demand agents of the technology patent transfers.

Key wordsTransaction Opportunities Prediction    Weighted Network    Structural Similarity    Content Similarity    BP Neural Network
收稿日期: 2018-03-12      出版日期: 2018-12-11
ZTFLH:  G306  
基金资助:*本文系国家自然科学基金项目“能源输入型城市能源生态系统建模及优化路径研究”(项目编号: 71371018)、北京市社会科学基金项目“要素异质性视角下京津冀现代制造产业转移路径研究”(项目编号: 15JGB124)和北京工业大学研究生科技基金资助“基于技术交易数据的京津冀科技协同创新潜力挖掘”(项目编号: ykj-2017-00437)的研究成果之一
引用本文:   
武玉英, 孙平, 何喜军, 蒋国瑞. 新能源领域专利转让加权网络中主体间技术交易机会预测*[J]. 数据分析与知识发现, 2018, 2(11): 73-79.
Wu Yuying,Sun Ping,He Xijun,Jiang Guorui. Predicting Transactions Among Agents in Patent Transfer Weighted Networks for New Energy. Data Analysis and Knowledge Discovery, 2018, 2(11): 73-79.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0254      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I11/73
预测模型 指标 ARC AUC
1 RA 0.0363 0.9054
2 LP 0.0651 0.8818
  模型预测精度
预测模型 指标 ARC AUC
1 RA+Cosine 0.0569 0.9428
2 RA+IPC 0.0853 0.8706
3 RA+Cosine+IPC 0.8706 0.9297
  结构-内容融合模型预测精度
未来链接 相似度 融合权重
国家电网公司 湖北省电力公司电力科学研究院 1.000 0.806
国网天津市电力公司 江苏中科宇泰光能科技有限公司 0.733 0.675
杭州南都电池有限公司 杭州南都能源科技有限公司 0.694 0.717
江苏博特新材料有限公司 国网湖北省电力公司经济技术研究院 0.659 0.679
江苏省建筑科学研究院有限公司 江苏博特新材料有限公司 0.659 0.635
中海油东方石化有限责任公司 中海油新能源(海南)生物能源化工有限公司 0.540 0.654
广东高航知识产权运营有限公司 国家电网公司 0.535 0.483
深圳市海川实业股份有限公司 深圳海川工程科技有限公司 0.522 0.592
鸿准精密工业股份有限公司 富士迈半导体精密工业(上海)有限公司 0.509 0.516
深圳市比克动力电池有限公司 郑州比克电池有限公司 0.489 0.681
北京维信诺科技有限公司 昆山维信诺显示技术有限公司 0.488 0.619
富士迈半导体精密工业(上海)有限公司 富准精密工业(深圳)有限公司 0.480 0.659
中海石油炼化有限责任公司 河海大学常州校区 0.469 0.441
中海石油炼化有限责任公司 上海宏力半导体制造有限公司 0.469 0.554
中海油东方石化有限责任公司 国网江苏省电力公司 0.469 0.442
中海油新能源(海南)生物能源化工有限公司 许继电气股份有限公司 0.469 0.549
国网浙江省电力公司 国网浙江省电力公司杭州供电公司 0.459 0.485
国网电力科学研究院武汉南瑞有限责任公司 中国电力科学研究院 0.450 0.523
南京南瑞集团公司 中国电力科学研究院 0.447 0.391
广东华博企业管理咨询有限公司 北京科技大学 0.443 0.400
  技术交易机会及其链接权重预测结果(前20)
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