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Exploring Technology Evolution Pathways Based on Link Prediction on Multiplex Network: Illustrated as CRISPR

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Wisdom, Well-Being, Win-Win (iConference 2024)

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Abstract

Exploring technology evolution pathways is essential since one can capture the best opportunity in a particular domain. Researchers attempt to exploit the critical trajectory from a historical perspective; however, only some steps forward to forecasting the future direction. This study proposes a new research framework to make reasonable predictions. Based on patents retrieved from DII, we construct a multiplex network consisting of co-citation and semantic layers. Specifically, we utilize the citation relationships between patents and extract technology topics with the Combined Topic Model(CTM), a powerful topic recognition tool. Subsequently, we employ the link prediction method to obtain future links and assemble them into a new co-citation network. We get credible predictions of future evolution trends by analyzing topics. To validate our framework, we take CRISPR, an emerging technology in gene editing, as a case study. Our experiments show that link prediction performs well in detecting future co-citation links, and the semantic layer further improves the prediction accuracy. We finally summarize seven potential directions and validate our predictions.

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Acknowledgments

We appreciate the anonymous reviewers’ careful examination of the manuscript and helpful comments. We appreciate Yihe Zhu, Yuanda Zhang, and Elysia Valentina for their help. We acknowledge support from the National Natural Science Foundation of China (Grant 72004169).

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, 430072, China

    Zizuo Cheng, Juan Tang, Jiaqi Yang & Ying Huang

  2. Centre for R&D Monitoring (ECOOM), Department of MSI, KU Leuven, 3000, Leuven, Belgium

    Ying Huang

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  1. Zizuo Cheng
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Correspondence to Ying Huang .

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Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

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Cheng, Z., Tang, J., Yang, J., Huang, Y. (2024). Exploring Technology Evolution Pathways Based on Link Prediction on Multiplex Network: Illustrated as CRISPR. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14597. Springer, Cham. https://doi.org/10.1007/978-3-031-57860-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-57860-1_8

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