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An integrated solution for detecting rising technology stars in co-inventor networks

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Abstract

Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber.

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Notes

  1. http://arnetminer.org.

  2. http://www.Biotec.tu-dresden.de/research/Schroeder/powergraphs/download-command-line-tool.html.

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Acknowledgements

This work is supported by the General Program of the National Natural Science Foundation of China (Grant Nos. 71673024, 71774012). The findings and observations in this paper are those of the authors and do not necessarily reflect the views of our supporters. The authors would like to thank colleagues from the Beijing Institute of Technology and Delft University of Technology. The authors would like to thank Yali Qiao for participating in the discussion of retrieval formulation in the process of data collection, the authors would also like to thank Scott W. Cunningham for providing the revision suggestions during Lin Zhu’s visit as a visiting scholar at Delft University of Technology.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Lin Zhu, Donghua Zhu, Xuefeng Wang & Zhinan Wang

  2. Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX, Delft, The Netherlands

    Lin Zhu & Scott W. Cunningham

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  1. Lin Zhu
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  2. Donghua Zhu
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  3. Xuefeng Wang
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  5. Zhinan Wang
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Corresponding author

Correspondence to Xuefeng Wang.

Appendices

Appendix 1

See Tables 5 and 6.

Table 5 Sensitivity analysis on the weight parameters α and β
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Table 6 Sensitivity analysis of the trend indexes to 2-year and 5-year windows
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Appendix 2: Samples of the identified different types of RT Stars

See Tables 7, 8 and Fig. 12.

Table 7 The evolution of 5 technology-oriented RT Stars in Cluster 3 and 1 top inventors from other Clusters
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Table 8 The technology distribution of 5 innovation-oriented RT Stars for all IPC6 classes
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Fig. 12
figure 12

Snapshot of powergraph representation of extended co-inventor network of Mironets Sergey (Blue dot). (Color figure online)

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Appendix 3: Supplementary codes

Supplementary codes associated with this article can be found, in the online version, at https://github.com/Lynn199021/Code.

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Zhu, L., Zhu, D., Wang, X. et al. An integrated solution for detecting rising technology stars in co-inventor networks. Scientometrics 121, 137–172 (2019). https://doi.org/10.1007/s11192-019-03194-w

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