IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v112y2017i1d10.1007_s11192-017-2388-9.html
   My bibliography  Save this article

Scientific collaboration patterns vary with scholars’ academic ages

Author

Listed:
  • Wei Wang

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Shuo Yu

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Teshome Megersa Bekele

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Xiangjie Kong

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Feng Xia

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

Abstract

Scientists may encounter many collaborators of different academic ages throughout their careers. Thus, they are required to make essential decisions to commence or end a creative partnership. This process can be influenced by strategic motivations because young scholars are pursuers while senior scholars are normally attractors during new collaborative opportunities. While previous works have mainly focused on cross-sectional collaboration patterns, this work investigates scientific collaboration networks from scholars’ local perspectives based on their academic ages. We aim to harness the power of big scholarly data to investigate scholars’ academic-age-aware collaboration patterns. From more than 621,493 scholars and 2,646,941 collaboration records in Physics and Computer Science, we discover several interesting academic-age-aware behaviors. First, in a given time period, the academic age distribution follows the long-tail distribution, where more than 80% scholars are of young age. Second, with the increasing of academic age, the degree centrality of scholars goes up accordingly, which means that senior scholars tend to have more collaborators. Third, based on the collaboration frequency and distribution between scholars of different academic ages, we observe an obvious homophily phenomenon in scientific collaborations. Fourth, the scientific collaboration triads are mostly consisted with beginning scholars. Furthermore, the differences in collaboration patterns between these two fields in terms of academic age are discussed.

Suggested Citation

  • Wei Wang & Shuo Yu & Teshome Megersa Bekele & Xiangjie Kong & Feng Xia, 2017. "Scientific collaboration patterns vary with scholars’ academic ages," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 329-343, July.
  • Handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2388-9
    DOI: 10.1007/s11192-017-2388-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2388-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-017-2388-9?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. Xiangjie Kong & Huizhen Jiang & Zhuo Yang & Zhenzhen Xu & Feng Xia & Amr Tolba, 2016. "Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    2. Borrett, Stuart R. & Moody, James & Edelmann, Achim, 2014. "The rise of Network Ecology: Maps of the topic diversity and scientific collaboration," Ecological Modelling, Elsevier, vol. 293(C), pages 111-127.
    3. Cassidy R. Sugimoto & Thomas J. Sugimoto & Andrew Tsou & Staša Milojević & Vincent Larivière, 2016. "Age stratification and cohort effects in scholarly communication: a study of social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 997-1016, November.
    4. Kamal Badar & Julie M. Hite & Naeem Ashraf, 2015. "Knowledge network centrality, formal rank and research performance: evidence for curvilinear and interaction effects," Post-Print hal-02945454, HAL.
    5. Ortega, José Luis, 2014. "Influence of co-authorship networks in the research impact: Ego network analyses from Microsoft Academic Search," Journal of Informetrics, Elsevier, vol. 8(3), pages 728-737.
    6. Kamal Badar & Julie M. Hite & Naeem Ashraf, 2015. "Knowledge network centrality, formal rank and research performance: evidence for curvilinear and interaction effects," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1553-1576, December.
    7. Katz, J. Sylvan & Martin, Ben R., 1997. "What is research collaboration?," Research Policy, Elsevier, vol. 26(1), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jacqueline Brown & Dakota Murray & Kyle Furlong & Emily Coco & Fabian Dablander, 2021. "A breeding pool of ideas: Analyzing interdisciplinary collaborations at the Complex Systems Summer School," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-16, February.
    2. Liang, Zhentao & Ba, Zhichao & Mao, Jin & Li, Gang, 2023. "Research complexity increases with scientists’ academic age: Evidence from library and information science," Journal of Informetrics, Elsevier, vol. 17(1).
    3. Kong, Xiangjie & Mao, Mengyi & Jiang, Huizhen & Yu, Shuo & Wan, Liangtian, 2019. "How does collaboration affect researchers’ positions in co-authorship networks?," Journal of Informetrics, Elsevier, vol. 13(3), pages 887-900.
    4. Jiaying Liu & Tao Tang & Xiangjie Kong & Amr Tolba & Zafer AL-Makhadmeh & Feng Xia, 2018. "Understanding the advisor–advisee relationship via scholarly data analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 161-180, July.
    5. Shanshan Wang & Kun Chen & Zhiyong Liu & Ren-Yong Guo & Jianshan Sun & Qiongjie Dai, 2019. "A data-driven approach for extracting and analyzing collaboration patterns at the interagent and intergroup levels in business process," Electronic Commerce Research, Springer, vol. 19(2), pages 451-470, June.
    6. Lu, Wei & Ren, Yan & Huang, Yong & Bu, Yi & Zhang, Yuehan, 2021. "Scientific collaboration and career stages: An ego-centric perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Liang, Guoqiang & Hou, Haiyan & Ding, Ying & Hu, Zhigang, 2020. "Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country," Journal of Informetrics, Elsevier, vol. 14(3).
    8. Guobin Chen & Tangsen Huang, 2019. "Community privacy estimation method based on key node method in space social Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    9. Song, Le & Ma, Yinghong, 2022. "Evaluating tacit knowledge diffusion with algebra matrix algorithm based social networks," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    10. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    11. Wu, Leyan & Yi, Fan & Bu, Yi & Lu, Wei & Huang, Yong, 2024. "Toward scientific collaboration: A cost-benefit perspective," Research Policy, Elsevier, vol. 53(2).
    12. Jun Zhang & Yan Hu & Zhaolong Ning & Amr Tolba & Elsayed Elashkar & Feng Xia, 2018. "AIRank: Author Impact Ranking through Positions in Collaboration Networks," Complexity, Hindawi, vol. 2018, pages 1-16, June.
    13. Chang, Ying-Han & Huang, Mu-Hsuan, 2023. "Analysis of factors affecting scientific migration move and distance by academic age, migrant type, and country: Migrant researchers in the field of business and management," Journal of Informetrics, Elsevier, vol. 17(1).
    14. António Correia & Hugo Paredes & Benjamim Fonseca, 2018. "Scientometric analysis of scientific publications in CSCW," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 31-89, January.
    15. Wang, Wei & Ren, Jing & Alrashoud, Mubarak & Xia, Feng & Mao, Mengyi & Tolba, Amr, 2020. "Early-stage reciprocity in sustainable scientific collaboration," Journal of Informetrics, Elsevier, vol. 14(3).
    16. Wei Wang & Xiaomei Bai & Feng Xia & Teshome Megersa Bekele & Xiaoyan Su & Amr Tolba, 2017. "From triadic closure to conference closure: the role of academic conferences in promoting scientific collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 177-193, October.
    17. Liyin Zhang & Yuchen Qian & Chao Ma & Jiang Li, 2023. "Continued collaboration shortens the transition period of scientists who move to another institution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1765-1784, March.
    18. Yu, Shuo & Alqahtani, Fayez & Tolba, Amr & Lee, Ivan & Jia, Tao & Xia, Feng, 2022. "Collaborative Team Recognition: A Core Plus Extension Structure," Journal of Informetrics, Elsevier, vol. 16(4).
    19. Lipeng Fan & Yuefen Wang & Shengchun Ding & Binbin Qi, 2020. "Productivity trends and citation impact of different institutional collaboration patterns at the research units’ level," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1179-1196, November.
    20. Jun Zhang & Zhaolong Ning & Xiaomei Bai & Xiangjie Kong & Jinmeng Zhou & Feng Xia, 2017. "Exploring time factors in measuring the scientific impact of scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1301-1321, September.

    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. Marian-Gabriel Hâncean & Matjaž Perc & Jürgen Lerner, 2021. "The coauthorship networks of the most productive European researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 201-224, January.
    2. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Jing Tu, 2019. "What connections lead to good scientific performance?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 587-604, February.
    4. Lu, Wei & Ren, Yan & Huang, Yong & Bu, Yi & Zhang, Yuehan, 2021. "Scientific collaboration and career stages: An ego-centric perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    5. Zhigao Liu & Yimei Yin & Weidong Liu & Michael Dunford, 2015. "Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 135-158, April.
    6. Yanqing Shi & Si Chen & Lele Kang, 2021. "Which questions are valuable in online Q&A communities? A question’s position in a knowledge network matters," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8239-8258, October.
    7. Zhu, Nibing & Liu, Chang & Yang, Zhilin, 2021. "Team Size, Research Variety, and Research Performance: Do Coauthors’ Coauthors Matter?," Journal of Informetrics, Elsevier, vol. 15(4).
    8. Yongjun Zhu & Lihong Quan & Pei‐Ying Chen & Meen Chul Kim & Chao Che, 2023. "Predicting coauthorship using bibliographic network embedding," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 388-401, April.
    9. Xiangjie Kong & Huizhen Jiang & Wei Wang & Teshome Megersa Bekele & Zhenzhen Xu & Meng Wang, 2017. "Exploring dynamic research interest and academic influence for scientific collaborator recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 369-385, October.
    10. Yan Yan & Jiancheng Guan, 2018. "How multiple networks help in creating knowledge: evidence from alternative energy patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 51-77, April.
    11. Konda, Bruhan & González‐Sauri, Mario & Cowan, Robin & Yashodha, Yashodha & Chellattan Veettil, Prakashan, 2021. "Social networks and agricultural performance: A multiplex analysis of interactions among Indian rice farmers," MERIT Working Papers 2021-030, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    12. Thomas C. Erren & J. Valérie Groß, 2016. "Research metrics: What about weighted citations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 315-316, April.
    13. Isabel Diez-Vial & Angeles Montoro-Sanchez, 2017. "Research evolution in science parks and incubators: foundations and new trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1243-1272, March.
    14. Fernando Martín-Alcázar & Marta Ruiz-Martínez & Gonzalo Sánchez-Gardey, 2019. "Assessing social capital in academic research teams: a measurement instrument proposal," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 917-935, November.
    15. Mengyang Wang & Lihe Chai, 2018. "Three new bibliometric indicators/approaches derived from keyword analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 721-750, August.
    16. Ramakrishnan Ramanathan, 2018. "Understanding Complexity: the Curvilinear Relationship Between Environmental Performance and Firm Performance," Journal of Business Ethics, Springer, vol. 149(2), pages 383-393, May.
    17. Marjan Cugmas & Anuška Ferligoj & Luka Kronegger, 2016. "The stability of co-authorship structures," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 163-186, January.
    18. Yun Liu & Mengya Zhang & Gupeng Zhang & Xiongxiong You, 2022. "Scientific elites versus other scientists: who are better at taking advantage of the research collaboration network?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3145-3166, June.
    19. Manning, Stephan, 2017. "The rise of project network organizations: Building core teams and flexible partner pools for interorganizational projects," Research Policy, Elsevier, vol. 46(8), pages 1399-1415.
    20. Hayashi, Takayuki, 2003. "Effect of R&D programmes on the formation of university-industry-government networks: comparative analysis of Japanese R&D programmes," Research Policy, Elsevier, vol. 32(8), pages 1421-1442, September.

    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:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2388-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.