IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v16y2022i3s1751157722000724.html
   My bibliography  Save this article

DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction

Author

Listed:
  • Jung, Sukhwan
  • Segev, Aviv

Abstract

Topic emergence detection aids in pinpointing prominent topics within a given domain, providing practical insights into all interested parties on where to focus the limited resources. This paper employs the network-based topic evolution approach to overcome limitations in text-based topic evolution, providing prospective topic emergence prediction capabilities by representing emergent topics by their ancestors. A descendant-aware clustering algorithm is proposed to generate non-exhaustive and overlapping clusters, utilizing the pace of collaborations and structural similarities between topics with iterative edge removal and addition processes. Over 100 datasets specific to a research topic were extracted from the Microsoft Academic Graph dataset for the experiments, where the proposed algorithm consistently outperformed existing clustering algorithms in generating clusters with a higher likelihood of being ancestors to an emergent topic up to three years in the future. Regression-based cluster filtering using five structural cluster features and topic cluster qualities showed that the prediction performance can be enhanced by automatically classifying undesirable clusters from previously known data. The results showed that the proposed algorithm can enhance topic emergence predictions on a wide range of research domains regardless of their maturities, popularities, and magnitudes without having access to the data in the predicted year, paving a road to prospective predictions on emergent topics.

Suggested Citation

  • Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:3:s1751157722000724
    DOI: 10.1016/j.joi.2022.101320
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157722000724
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2022.101320?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Battistella, Cinzia, 2014. "The organisation of Corporate Foresight: A multiple case study in the telecommunication industry," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 60-79.
    2. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    3. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    4. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    5. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    6. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Sven E. Hug & Michael Ochsner & Martin P. Brändle, 2017. "Citation analysis with microsoft academic," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 371-378, April.
    8. Chen, Baitong & Tsutsui, Satoshi & Ding, Ying & Ma, Feicheng, 2017. "Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 11(4), pages 1175-1189.
    9. Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
    10. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
    11. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    12. Yi Zhang & Guangquan Zhang & Donghua Zhu & Jie Lu, 2017. "Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1925-1939, August.
    13. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    2. Leonid Gokhberg & Dirk Meissner & Ilya Kuzminov, 2023. "What semantic analysis can tell us about long term trends in the global STI policy agenda," The Journal of Technology Transfer, Springer, vol. 48(6), pages 2249-2277, December.
    3. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    4. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    5. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    6. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Serhat Burmaoglu & Olivier Sartenaer & Alan Porter & Munan Li, 2019. "Analysing the theoretical roots of technology emergence: an evolutionary perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 97-118, April.
    8. Lu, Kun & Yang, Guancan & Wang, Xue, 2022. "Topics emerged in the biomedical field and their characteristics," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    9. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    10. Porter, Alan L. & Garner, Jon & Carley, Stephen F. & Newman, Nils C., 2019. "Emergence scoring to identify frontier R&D topics and key players," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 628-643.
    11. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    12. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    13. Woo, Seokkyun & Youtie, Jan & Ott, Ingrid & Scheu, Fenja, 2021. "Understanding the long-term emergence of autonomous vehicles technologies," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    14. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    15. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    16. Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
    17. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    18. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    19. Seokbeom Kwon & Jan Youtie & Alan Porter & Nils Newman, 2024. "How does regulatory uncertainty shape the innovation process? Evidence from the case of nanomedicine," The Journal of Technology Transfer, Springer, vol. 49(1), pages 262-302, February.
    20. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:16:y:2022:i:3:s1751157722000724. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.