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Predicting future technological convergence patterns based on machine learning using link prediction

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Abstract

Technological convergence among different industries is an important source of innovation and economic growth. In this study, we propose a new framework for predicting patterns of technological convergence in two different industries. We first construct an inter-process communication co-occurrence network based on association rule mining. We then use a machine learning approach with various link prediction indices to predict future technological convergence patterns. Next, we use latent Dirichlet allocation (LDA) topic modeling to identify the keywords associated with technologies that are predicted to converge. We apply our proposed framework to a dataset of patents from the United States Patent and Trademark Office from 2012 to 2014 in the fields of chemical engineering and environmental technology. The empirical analysis results show that the prediction over a 4-year time interval using the random forest model achieves the highest performance. Moreover, the LDA topic modeling results indicate that the keywords “membrane,” “air,” “separation,” “catalyst,” “gas,” “exhaust,” and “particle” are descriptions of technologies that are likely to converge. This study is expected to contribute to technological and economic growth by predicting new technological fields that are likely to emerge in the future, and hence the directions that firms focusing on technological advancement should prepare for.

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References

  • Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230

    Article  Google Scholar 

  • Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp. 207–216).

  • Al Hasan, M., & Zaki, M. J. (2011). A survey of link prediction in social networks. In Social network data analytics (pp. 243–275). New York: Springer.

  • Allarakhia, M., & Walsh, S. (2012). Analyzing and organizing nanotechnology development: Application of the institutional analysis development framework to nanotechnology consortia. Technovation, 32(3), 216–226

    Article  Google Scholar 

  • Antonucci, T., & Pianta, M. (2002). Employment effects of product and process innovation in Europe. International Review of Applied Economics, 16(3), 295–307

    Article  Google Scholar 

  • Athreye, S., & Keeble, D. (2000). Technological convergence, globalisation and ownership in the UK computer industry. Technovation, 20(5), 227–245

    Article  Google Scholar 

  • Back, B., Sere, K., & Vanharanta, H. (1998). Managing complexity in large data bases using self-organizing maps. Accounting, Management and Information Technologies, 8(4), 191–210

    Article  Google Scholar 

  • Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512

    Article  MathSciNet  MATH  Google Scholar 

  • Baruffaldi, S. H., & Simeth, M. (2020). Patents and knowledge diffusion: The effect of early disclosure. Research Policy, 49(4), 103927

    Article  Google Scholar 

  • Bass, J. I. F., Diallo, A., Nelson, J., Soto, J. M., Myers, C. L., & Walhout, A. J. (2013). Using networks to measure similarity between genes: Association index selection. Nature Methods, 10(12), 1169–1176

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022

    MATH  Google Scholar 

  • Bliss, C. A., Frank, M. R., Danforth, C. M., & Dodds, P. S. (2014). An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science, 5(5), 750–764

    Article  MathSciNet  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32

    Article  MATH  Google Scholar 

  • Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453

    Article  Google Scholar 

  • Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7), 1775–1781

    Article  Google Scholar 

  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357

    Article  MATH  Google Scholar 

  • Chebotarev, P., & Shamis, E. (2006). The matrix-forest theorem and measuring relations in small social groups. arXiv preprint math/0602070.

  • Chiru, C. G., Rebedea, T., & Ciotec, S. (2014). Comparison between LSA-LDA-Lexical Chains. In WEBIST (2) (pp. 255–262).

  • Choi, H. S., Lee, W. S., & Sohn, S. Y. (2017). Analyzing research trends in personal information privacy using topic modeling. Computers & Security, 67, 244–253

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297

    Article  MATH  Google Scholar 

  • Dolata, U. (2009). Technological innovations and sectoral change: transformative capacity, adaptability, patterns of change: An analytical framework. Research Policy, 38(6), 1066–1076

    Article  Google Scholar 

  • Dong, L., Li, Y., Yin, H., Le, H., & Rui, M. (2013). The algorithm of link prediction on social network. Mathematical Problems in Engineering, 2013, 1–7.

  • Dosi, G. (1982). Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy, 11(3), 147–162

    Article  Google Scholar 

  • Du Jardin, P., & Séverin, E. (2011). Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model. Decision Support Systems, 51(3), 701–711

    Article  Google Scholar 

  • Gwak, J. H., & Sohn, S. Y. (2017). Identifying the trends in wound-healing patents for successful investment strategies. PLoS ONE, 12(3), e0174203

    Article  Google Scholar 

  • Hacklin, F. (2007). Management of convergence in innovation: Strategies and capabilities for value creation beyond blurring industry boundaries. Springer.

    Google Scholar 

  • Hacklin, F., & Wallin, M. W. (2013). Convergence and interdisciplinarity in innovation management: A review, critique, and future directions. The Service Industries Journal, 33(7–8), 774–788

    Article  Google Scholar 

  • Hu, R., Skea, J., & Hannon, M. J. (2018). Measuring the energy innovation process: An indicator framework and a case study of wind energy in China. Technological Forecasting and Social Change, 127, 227–244

    Article  Google Scholar 

  • Iwai, K. (2000). A contribution to the evolutionary theory of innovation, imitation and growth. Journal of Economic Behavior & Organization, 43(2), 167–198

    Article  Google Scholar 

  • Jaccard, P. (1901). Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547–579

    Google Scholar 

  • Jeh, G., & Widom, J. (2002). SimRank: A measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 538–543). ACM.

  • Jeong, S., Kim, J. C., & Choi, J. Y. (2015). Technology convergence: What developmental stage are we in? Scientometrics, 104(3), 841–871

    Article  Google Scholar 

  • Jones, W. P., & Furnas, G. W. (1987). Pictures of relevance: A geometric analysis of similarity measures. Journal of the American Society for Information Science, 38(6), 420

    Article  Google Scholar 

  • Kim, E., Cho, Y., & Kim, W. (2014). Dynamic patterns of technological convergence in printed electronics technologies: Patent citation network. Scientometrics, 98(2), 975–998

    Article  Google Scholar 

  • Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39–43

    Article  MATH  Google Scholar 

  • Kim, J., & Lee, S. (2017). Forecasting and identifying multi-technology convergence based on patent data: The case of IT and BT industries in 2020. Scientometrics, 111(1), 47–65

    Article  Google Scholar 

  • Kim, Y. J., Jung, U., & Jeong, S. K. (2009). A study on the status and supporting strategy of national R&D programs related to the convergence technology. Journal of Korea Technology Innovation Society, 12, 413–429

    Google Scholar 

  • Ko, N., Yoon, J., & Seo, W. (2014). Analyzing interdisciplinarity of technology fusion using knowledge flows of patents. Expert Systems with Applications, 41(4), 1955–1963

    Article  Google Scholar 

  • Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28(3), 247–268

    Article  Google Scholar 

  • Kwon, Y. I., & Jeong, D. H. (2014). Technology relevance analysis between wind power energy-fuel cell-green car using network analysis, IPC map. Collnet Journal of Scientometrics and Information Management, 8(1), 109–121

    Article  Google Scholar 

  • Lee, W. S., Han, E. J., & Sohn, S. Y. (2015). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change, 100, 317–329

    Article  Google Scholar 

  • Leicht, E. A., Holme, P., & Newman, M. E. (2006). Vertex similarity in networks. Physical Review E, 73(2), 026120

    Article  Google Scholar 

  • Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the Association for Information Science and Technology, 58(7), 1019–1031

    Google Scholar 

  • Lu, L., Jin, C. H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. Physical Review E, 80(4), 046122

    Article  Google Scholar 

  • Martin, B. R., Nightingale, P., & Yegros-Yegros, A. (2012). Science and technology studies: Exploring the knowledge base. Research Policy, 41(7), 1182–1204.

  • Newman, M. E. (2001). Clustering and preferential attachment in growing networks. Physical review E, 64(2), 025102

    Article  Google Scholar 

  • Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A. L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297(5586), 1551–1555

    Article  Google Scholar 

  • Roco, M. C., & Bainbridge, W. S. (2002). Converging technologies for improving human performance: Integrating from the nanoscale. Journal of Nanoparticle Research, 4(4), 281–295

    Article  Google Scholar 

  • Rodriguez, A., Kim, B., Turkoz, M., Lee, J. M., Coh, B. Y., & Jeong, M. K. (2015). New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network. Scientometrics, 103(2), 565–581

    Article  Google Scholar 

  • Salton, G., & McGill, M. J. (1986). Introduction to modern information retrieval. Facet Publishing.

    MATH  Google Scholar 

  • Sherkat, E., Rahgozar, M., & Asadpour, M. (2015). Structural link prediction based on ant colony approach in social networks. Physica A: Statistical Mechanics and its Applications, 419, 80–94

    Article  Google Scholar 

  • Sorensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol. Skr., 5, 1–34

    Google Scholar 

  • Sun, M., & Zheng, H. (2018). Topic detection for post bar based on LDA model. In International conference of pioneering computer scientists, engineers and educators (pp. 136–149). Singapore: Springer.

  • Tang, J., Chang, S., Aggarwal, C., & Liu, H. (2015). Negative link prediction in social media. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 87–96). ACM.

  • Wang, P., Xu, B., Wu, Y., & Zhou, X. (2015). Link prediction in social networks: The state-of-the-art. Science China Information Sciences, 58(1), 1–38

    Google Scholar 

  • Wolbring, G. (2008). Why NBIC? Why human performance enhancement? European Journal of Social Science Research, 21, 25–40.

  • Zhou, T., Lü, L., & Zhang, Y. C. (2009). Predicting missing links via local information. The European Physical Journal B-Condensed Matter and Complex Systems, 71(4), 623–630

    Article  MATH  Google Scholar 

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Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016R1A2A1A05005270). The earlier Korean version of this paper was awarded first place in the 13th KMAC Management Innovation Research Paper Competition in 2017.

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Authors and Affiliations

  1. Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-dong, Seoul, 120-749, Republic of Korea

    Joon Hyung Cho, Jungpyo Lee & So Young Sohn

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  1. Joon Hyung Cho
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  2. Jungpyo Lee
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  3. So Young Sohn
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Corresponding author

Correspondence to So Young Sohn.

Appendices

Appendix 1

See Table 8.

Table 8 IPC for each industry
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Appendix 2

See Table 9.

Table 9 IPC pairs of predicted converge in the future
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Cho, J.H., Lee, J. & Sohn, S.Y. Predicting future technological convergence patterns based on machine learning using link prediction. Scientometrics 126, 5413–5429 (2021). https://doi.org/10.1007/s11192-021-03999-8

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