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An editorial of “AI + informetrics”: multi-disciplinary interactions in the era of big data

Author

Listed:
  • Yi Zhang

    (University of Technology Sydney)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

  • Philipp Mayr

    (GESIS-Leibniz Institute for the Social Sciences)

  • Arho Suominen

    (Tampere University)

Abstract

No abstract is available for this item.

Suggested Citation

  • Yi Zhang & Chengzhi Zhang & Philipp Mayr & Arho Suominen, 2022. "An editorial of “AI + informetrics”: multi-disciplinary interactions in the era of big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6503-6507, November.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:11:d:10.1007_s11192-022-04561-w
    DOI: 10.1007/s11192-022-04561-w
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    References listed on IDEAS

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    1. Xiaowen Xi & Jiaqi Wei & Ying Guo & Weiyu Duan, 2022. "Academic collaborations: a recommender framework spanning research interests and network topology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6787-6808, November.
    2. Xin An & Xin Sun & Shuo Xu, 2022. "Important citations identification with semi-supervised classification model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6533-6555, November.
    3. Matthias Kuppler, 2022. "Predicting the future impact of Computer Science researchers: Is there a gender bias?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6695-6732, November.
    4. Arash Hajikhani & Arho Suominen, 2022. "Mapping the sustainable development goals (SDGs) in science, technology and innovation: application of machine learning in SDG-oriented artefact detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6661-6693, November.
    5. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    6. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    7. Yang Zhang & Rongying Zhao & Yufei Wang & Haihua Chen & Adnan Mahmood & Munazza Zaib & Wei Emma Zhang & Quan Z. Sheng, 2022. "Correction to: Towards employing native information in citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6579-6579, November.
    8. Shiyun Wang & Jin Mao & Yujie Cao & Gang Li, 2022. "Integrated knowledge content in an interdisciplinary field: identification, classification, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6581-6614, November.
    9. Yang Zhang & Rongying Zhao & Yufei Wang & Haihua Chen & Adnan Mahmood & Munazza Zaib & Wei Emma Zhang & Quan Z. Sheng, 2022. "Towards employing native information in citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6557-6577, November.
    10. Zafar Ali & Guilin Qi & Pavlos Kefalas & Shah Khusro & Inayat Khan & Khan Muhammad, 2022. "SPR-SMN: scientific paper recommendation employing SPECTER with memory network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6763-6785, November.
    11. Hongshu Chen & Xinna Song & Qianqian Jin & Ximeng Wang, 2022. "Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6637-6660, November.
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