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CLC number: TP391

On-line Access: 2015-07-06

Received: 2014-11-16

Revision Accepted: 2015-05-26

Crosschecked: 2015-06-23

Cited: 5

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Citations:  Bibtex RefMan EndNote GB/T7714

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

http://doi.org/10.1631/FITEE.1400394


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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Abstract: 
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.

基于异构专利网络特征的有影响力发明人员的排名与发现

目的:专利是发现新技术信息独特的信息源,也是竞争情报重要的信息源之一。目前,如何评估科学研究人员的贡献及其研究价值逐渐成为一个新兴的研究热点。本文提出一种利用专利数据异构网络对专利发明人员进行影响力排序的算法。
创新点:传统对发明人员进行分析的方法是对发明人的专利数量进行统计分析,但这种方法不够全面。本文提出的基于规则的方法,设计结合网络拓扑结构和专利数据特点,排序过程不断迭代直至符合收敛条件。与传统方法相比,该方法充分利用异构网络中的信息。实验结果表明本算法不仅能有效挖掘具有高影响力的发明人员,而且收敛速度更快、效率更高。
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)。
结论:本文针对专利数据组成的异构网络,提出异构网络中实体的排序算法。制定了用于影响力排序的规则集合并进行迭代求解。同时,利用LDA主题模型实现发明人实体的兴趣分布与发现。在真实专利数据集上的实验表明,本文提出的算法具有较好的性能与灵活性。

关键词:专利异构网络;影响力;基于规则排序

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