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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 73-79    DOI: 10.11925/infotech.2096-3467.2018.0254
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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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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     
Received: 12 March 2018      Published: 11 December 2018
ZTFLH:  G306  

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0254     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I11/73

预测模型 指标 ARC AUC
1 RA 0.0363 0.9054
2 LP 0.0651 0.8818
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
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
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