, Volume 121, Issue 1, pp 137–172 | Cite as

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

  • Lin Zhu
  • Donghua Zhu
  • Xuefeng WangEmail author
  • Scott W. Cunningham
  • Zhinan Wang


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.


Rising technology stars Co-inventor networks Social potential Technology performance Innovation caliber Tech mining 



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.


  1. Bergstrom, C. (2007). Measuring the value and prestige of scholarly journals. College and Research Libraries News, 68(5), 314–316.CrossRefGoogle Scholar
  2. Bordons, M., Fernández, M., & Gómez, I. (2002). Advantages and limitations in the use of impact factor measures for the assessment of research performance. Scientometrics, 53(2), 195–206.CrossRefGoogle Scholar
  3. Braun, T., Glänzel, W., & Schubert, A. (2006). A Hirsch-type index for journals. Scientometrics, 69(1), 169–173.CrossRefGoogle Scholar
  4. Breschi, S., & Catalini, C. (2010). Tracing the links between science and technology: An exploratory analysis of scientists’ and inventors’ networks. Research Policy, 39(1), 14–26.CrossRefGoogle Scholar
  5. Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3, 1–27.MathSciNetzbMATHGoogle Scholar
  6. Carley, S. F., Newman, N. C., Porter, A. L., & Garner, J. G. (2018). An indicator of technical emergence. Scientometrics, 115(3), 1–15.Google Scholar
  7. Chen, Y. C., Lin, Y. Y., Lin, H. E., & Mcdonough, E. F. (2012). Does transformational leadership facilitate technological innovation? the moderating roles of innovative culture and incentive compensation. Asia Pacific Journal of Management, 29(2), 239–264.CrossRefGoogle Scholar
  8. Cheng, H., Li, L. I., Dong-Qin, L. I., & University, S. T. (2015). Innovation or technology importing: determinants and interactive effect based on technical capability. Scientific Management Research, 33(4), 72–75.Google Scholar
  9. Choi, S., & Park, H. (2016). Investigation of strategic changes using patent co-inventor network analysis: The case of samsung electronics. Sustainability, 8(12), 1315–1327.CrossRefGoogle Scholar
  10. Daud, A., Abbasi, R., & Muhammad, F. (2013). Finding rising stars in social networks. Database Systems for Advanced Applications (LNCS), 7825, 13–24.CrossRefGoogle Scholar
  11. Daud, A., Ahmad, M., Malik, M. S. I., & Che, D. (2015). Using machine learning techniques for rising star prediction in co-author network. Scientometrics, 102(2), 1687–1711.CrossRefGoogle Scholar
  12. Daud, A., Aljohani, N. R., Abbasi, R. A., Rafique, Z., Amjad, T., Dawood, H., & Alyoubi, K. H. (2017). Finding rising stars in co-author networks via weighted mutual influence. In International conference on World Wide Web companion. International World Wide Web conferences steering committee (pp. 33–41).Google Scholar
  13. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227.CrossRefGoogle Scholar
  14. Davoodi, E., Kianmehr, K., & Afsharchi, M. (2013). A semantic social network-based expert recommender system. Applied Intelligence, 39(1), 1–13.CrossRefGoogle Scholar
  15. Ding, F., Liu, Y., Chen, X., & Chen, F. (2018). Rising star evaluation in heterogeneous social network. IEEE Access, 6, 29436–29443.CrossRefGoogle Scholar
  16. Dornbusch, F., & Neuhäusler, P. (2015). Composition of inventor teams and technological progress—The role of collaboration between academia and industry. Research Policy, 44(7), 1360–1375.CrossRefGoogle Scholar
  17. Edquist, C., & Hommen, L. (1999). Systems of innovation: theory and policy for the demand side 1. Technology in Society, 21(1), 63–79.CrossRefGoogle Scholar
  18. Ejermo, O., & Karlsson, C. (2006). Interregional inventor networks as studied by patent coinventorships. Research Policy, 35(3), 412–430.CrossRefGoogle Scholar
  19. Guan, J., & Zuo, K. (2014). A cross-country comparison of innovation efficiency. Scientometrics, 100(2), 541–575.CrossRefGoogle Scholar
  20. Gulbrandsen, M., & Smeby, J. C. (2005). Industry funding and university professors’ research performance. Research Policy, 34(6), 932–950.CrossRefGoogle Scholar
  21. Hu, A. G., & Jaffe, A. B. (2001). Patent citations and international knowledge flow: The cases of Korea and Taiwan. International Journal of Industrial Organization, 21(6), 849–880.CrossRefGoogle Scholar
  22. Hu, M. C. (2012). Technological innovation capabilities in the thin film transistor-liquid crystal display industries of Japan, Korea, and Taiwan. Research Policy, 41(3), 541–555.CrossRefGoogle Scholar
  23. Jun, S., Sung Park, S., & Sik Jang, D. (2012). Technology forecasting using matrix map and patent clustering. Industrial Management and Data Systems, 112(5), 786–807.CrossRefGoogle Scholar
  24. Kay, L., Newman, N., Youtie, J., Porter, A. L., & Rafols, I. (2014). Patent overlay mapping: Visualizing technological distance. Journal of the Association for Information Science and Technology, 65(12), 2432–2443.CrossRefGoogle Scholar
  25. Li, X. L., Foo, C. S., Tew, K. L., & Ng, S. K. (2009). Searching for rising stars in bibliography networks. Database Systems for Advanced Applications, 5463, 288–292.CrossRefGoogle Scholar
  26. Liu, X., Wan, X. P., & Ma, F. C. (2015). Detecting of technology innovation trends based on patent data: Theory and methods. Information Science, 33(12), 20–26.Google Scholar
  27. Lu, J., & Han, G. (2002). Osculating value method of business technology innovation capacity evaluation. Science Research Management, 23(1), 54–57.Google Scholar
  28. Lukatch, R., & Plasmans, J. (2002). Measuring knowledge spillovers using patent citations: Evidence from Belgian firms’ data. Social Science Electronic Publishing, 6(7), 1–26.Google Scholar
  29. Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650–1654.CrossRefGoogle Scholar
  30. Oh, G., Kim, H. Y., & Park, A. (2017). Analysis of technological innovation based on citation information. Quality & Quantity, 51(3), 1065–1091.CrossRefGoogle Scholar
  31. Ozcan, S., & Islam, N. (2017). Patent information retrieval: Approaching a method and analysing nanotechnology patent collaborations. Scientometrics, 111(2), 941–970.CrossRefGoogle Scholar
  32. Panagopoulos, G., Tsatsaronis, G., & Varlamis, I. (2017). Detecting rising stars in dynamic collaborative networks. Journal of Informetrics, 11(1), 198–222.CrossRefGoogle Scholar
  33. Panaretos, J., & Malesios, C. (2009). Assessing scientific research performance and impact with single indices. Scientometrics, 81(3), 635–670.CrossRefGoogle Scholar
  34. Peri, G. (2005). Determinants of knowledge flows and their effect on innovation. Review of Economics and Statistics, 87(2), 308–322.CrossRefGoogle Scholar
  35. Porter, A., Cohen, A., David Roessner, J., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147.CrossRefGoogle Scholar
  36. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.CrossRefzbMATHGoogle Scholar
  37. Roberto, J. L., O͂noro-Rubio, D., Pedro, G. J., & Saturnino, M. B. (2012). Fast reciprocal nearest neighbors clustering. Signal Processing, 92(1), 270–275.CrossRefGoogle Scholar
  38. Royer, L., Reimann, M., Andreopoulos, B., & Schroeder, M. (2008). Unraveling protein networks with power graph analysis. PLoS Computational Biology, 4(7), 1–17.MathSciNetCrossRefGoogle Scholar
  39. Royle, P., & Over, R. (1994). The use of bibliometric indicators to measure the research productivity of Australian academics. Australian Academic and Research Libraries, 25(2), 77–88.CrossRefGoogle Scholar
  40. Sharma, B., Boet, S., Grantcharov, T., Shin, E., Barrowman, N. J., & Bould, M. D. (2013). The h-index outperforms other bibliometrics in the assessment of research performance in general surgery: A province-wide study. Surgery, 153(4), 493–501.CrossRefGoogle Scholar
  41. Singh, J. (2005). Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51(5), 756–770.CrossRefzbMATHGoogle Scholar
  42. Sorenson, O., Rivkin, J. W., & Fleming, L. (2006). Complexity, networks and knowledge flow. Research Policy, 35(7), 994–1017.CrossRefGoogle Scholar
  43. Tsatsaronis, G., Varlamis, I., Torge, S., Reimann, M., Nørvåg, K., Schroeder, M., et al. (2011). How to become a group leader? or modeling author types based on graph mining. LNCS, 6966, 15–26.Google Scholar
  44. van der Wouden, F., & Rigby, D. (2017). Co-inventor Networks and Knowledge Production in Specialized and Diversified Cities. In Papers in evolutionary economic geography (pp. 1–27).Google Scholar
  45. Westerheijden, D. (1999). Innovation indicators in science and technology evaluation: Comments from a higher education point of view. Scientometrics, 45(3), 445–453.CrossRefGoogle Scholar
  46. Wongel, H. (2005). The reform of the INC-consequences for the users. World Patent Information, 27(3), 227–231.CrossRefGoogle Scholar
  47. Wu, C. Y. (2014). Comparisons of technological innovation capabilities in the solar photovoltaic industries of Taiwan, China, and Korea. Scientometrics, 98(1), 429–446.CrossRefGoogle Scholar
  48. Yeo, W., Kim, S., Park, H., & Kang, J. (2015). A bibliometric method for measuring the degree of technological innovation. Technological Forecasting and Social Change, 95, 152–162.CrossRefGoogle Scholar
  49. Yun, C., Chunfang, T., & Li, Y. (2012). Technological innovation capability evaluation index system research for small and medium sized technology enterprises. Science and Technology Progress and Policy, 29(2), 110–112.Google Scholar
  50. Zhang, C., Liu, C., Yu, L., Zhang, Z. K., & Zhou, T. (2017a). Identifying the academic rising stars via pairwise citation increment ranking, Asia-Pacific Web (pp. 475–483). Cham: Springer.Google Scholar
  51. Zhang, J., Ning, Z., Bai, X., Wang, W., Yu, S., & Xia, F. (2016a). Who are the rising stars in academia? In Digital libraries. IEEE (pp. 211–212).Google Scholar
  52. Zhang, J., Xia, F., Wang, W., Bai, X., Yu, S., Bekele, T. M., & Peng, Z. (2016b). CocaRank: A collaboration caliber-based method for finding academic rising stars. In International conference companion on World Wide Web. International World Wide Web conferences steering committee (pp. 395–400).Google Scholar
  53. Zhang, Y., Qian, Y., Huang, Y., Guo, Y., Zhang, G., & Lu, J. (2017b). An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation. Scientometrics, 111(3), 1–22.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina
  2. 2.Faculty of Technology, Policy and ManagementDelft University of TechnologyDelftThe Netherlands

Personalised recommendations