Full Text:   <1207>

Summary:  <401>

CLC number: TP391

On-line Access: 2015-07-06

Received: 2014-11-16

Revision Accepted: 2015-05-26

Crosschecked: 2015-06-23

Cited: 5

Clicked: 2367

Citations:  Bibtex RefMan EndNote GB/T7714

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.7 P.568-578


Using heterogeneous patent network features to rank and discover influential inventors

Author(s):  Yong-ping Du, Chang-qing Yao, Nan Li

Affiliation(s):  College of Computer Science, Beijing University of Technology, Beijing 100124, China; more

Corresponding email(s):   ypdu@bjut.edu.cn

Key Words:  Heterogeneous patent network, Influence, Rule-based ranking

Yong-ping Du, Chang-qing Yao, Nan Li. Using heterogeneous patent network features to rank and discover influential inventors[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 568-578.

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author="Yong-ping Du, Chang-qing Yao, Nan Li",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%A Chang-qing Yao
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%DOI 10.1631/FITEE.1400394

T1 - Using heterogeneous patent network features to rank and discover influential inventors
A1 - Yong-ping Du
A1 - Chang-qing Yao
A1 - Nan Li
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1400394

Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not applicable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor’s interesting topics using a latent Dirichlet allocation (LDA) model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.

The research is well conducted. The paper is also well organized and reported.


方法:不同于传统的排序方法,本文提出的Inventor-Ranking排序算法是一种基于规则的实体排序方法。该方法通过迭代使用这些规则得到排序结果。排序模型建立在发明人员和专利的相互影响进行排序的基础上(图3)。使用本算法和PageRank算法排序Top 10的发明人员(表2)。实验结果表明,Inventor-Ranking算法比PageRank算法收敛更快(图10)。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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