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h-Index-based link prediction methods in citation network

Author

Listed:
  • Wen Zhou

    (Shanghai University
    RISE SICS)

  • Jiayi Gu

    (Shanghai University)

  • Yifan Jia

    (Shanghai University)

Abstract

Link prediction implies the mining of the missing links in networks or prediction of the next node pair to be connected by a link. Link prediction is useful for mining information in citation networks, and most of the existing related studies commonly use degree rather than more advanced methods to measure the importance of nodes. However, such a method cannot easily measure the importance of a paper in reality; some papers have high degree in citation networks but are not very influential. This issue restricts the performance of the link prediction methods applied to citation networks. The current study analyzed h-type indices, which are more suitable than degree for measuring the importance of citation network nodes. We propose two h-index-based link prediction methods. Experiments conducted on real citation networks demonstrate that the use of h-type index to measure the importance of nodes in citation networks can significantly improve the prediction accuracy of link prediction methods.

Suggested Citation

  • Wen Zhou & Jiayi Gu & Yifan Jia, 2018. "h-Index-based link prediction methods in citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 381-390, October.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2867-7
    DOI: 10.1007/s11192-018-2867-7
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    References listed on IDEAS

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    Cited by:

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    2. Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
    3. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    4. Guoqiang Liang & Haiyan Hou & Xiaodan Lou & Zhigang Hu, 2019. "Qualifying threshold of “take-off” stage for successfully disseminated creative ideas," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1193-1208, September.
    5. Ting Xiong & Liang Zhou & Ying Zhao & Xiaojuan Zhang, 2022. "Mining semantic information of co-word network to improve link prediction performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 2981-3004, June.
    6. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.

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