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Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents

Author

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  • Xiaoling Sun

    (Dalian University of Technology)

  • Kun Ding

    (Dalian University of Technology)

Abstract

Knowledge memes are the cultural equivalent of genes that play an important role in the evolution of knowledge. In this paper, we are trying to identify and tracking scientific and technological knowledge memes, and infer the relationship between science and technology at micro-level. A new carbon nanomaterial—graphene is taken as an example, and publications and patents are used as data sources for the representation of science and technology. Citation networks of publications and patents are constructed, on which a knowledge meme discovery algorithm is used, in order to identify memes that play a key role in the evolution of scientific and technological knowledge. Then the diffusion and co-occurrence of knowledge memes are shown, and a word embedding model is used to track the semantic change of the memes. The research could provide guidance for promoting knowledge innovation and making research policy.

Suggested Citation

  • Xiaoling Sun & Kun Ding, 2018. "Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1735-1748, September.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:3:d:10.1007_s11192-018-2836-1
    DOI: 10.1007/s11192-018-2836-1
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    References listed on IDEAS

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    2. 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.
    3. Yan Qi & Xin Zhang & Zhengyin Hu & Bin Xiang & Ran Zhang & Shu Fang, 2022. "Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5519-5550, September.
    4. Yongchang Wei & Lei Chen & Yu Qi & Can Wang & Fei Li & Haorong Wang & Fangyu Chen, 2019. "A Complex Network Method in Criticality Evaluation of Air Quality Standards," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    5. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).

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