IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v104y2015i1d10.1007_s11192-015-1597-3.html
   My bibliography  Save this article

How we collaborate: characterizing, modeling and predicting scientific collaborations

Author

Listed:
  • Xiaoling Sun

    (Dalian University of Technology)

  • Hongfei Lin

    (Dalian University of Technology)

  • Kan Xu

    (Dalian University of Technology)

  • Kun Ding

    (Dalian University of Technology)

Abstract

The large amounts of publicly available bibliographic repositories on the web provide us great opportunities to study the scientific behaviors of scholars. This paper aims to study the way we collaborate, model the dynamics of collaborations and predict future collaborations among authors. We investigate the collaborations in three disciplines including physics, computer science and information science,and different kinds of features which may influence the creation of collaborations. Path-based features are found to be particularly useful in predicting collaborations. Besides, the combination of path-based and attribute-based features achieves almost the same performance as the combination of all features considered. Inspired by the findings, we propose an agent-based model to simulate the dynamics of collaborations. The model merges the ideas of network structure and node attributes by leveraging random walk mechanism and interests similarity. Empirical results show that the model could reproduce a number of realistic and critical network statistics and patterns. We further apply the model to predict collaborations in an unsupervised manner and compare it with several state-of-the-art approaches. The proposed model achieves the best predictive performance compared with the random baseline and other approaches. The results suggest that both network structure and node attributes may play an important role in shaping the evolution of collaboration networks.

Suggested Citation

  • Xiaoling Sun & Hongfei Lin & Kan Xu & Kun Ding, 2015. "How we collaborate: characterizing, modeling and predicting scientific collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 43-60, July.
    • Handle: RePEc:spr:scient:v:104:y:2015:i:1:d:10.1007_s11192-015-1597-3
    DOI: 10.1007/s11192-015-1597-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-015-1597-3
    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/s11192-015-1597-3?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. Abbasi, Alireza & Hossain, Liaquat & Leydesdorff, Loet, 2012. "Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 403-412.
    2. Haiyan Hou & Hildrun Kretschmer & Zeyuan Liu, 2008. "The structure of scientific collaboration networks in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(2), pages 189-202, May.
    3. Christopher McCarty & James W. Jawitz & Allison Hopkins & Alex Goldman, 2013. "Predicting author h-index using characteristics of the co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 467-483, August.
    4. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    5. Dalibor Fiala & François Rousselot & Karel Ježek, 2008. "PageRank for bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 135-158, July.
    6. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    7. Liu, Yuxian & Rousseau, Ronald & Guns, Raf, 2013. "A layered framework to study collaboration as a form of knowledge sharing and diffusion," Journal of Informetrics, Elsevier, vol. 7(3), pages 651-664.
    8. He, Bing & Ding, Ying & Tang, Jie & Reguramalingam, Vignesh & Bollen, Johan, 2013. "Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective," Journal of Informetrics, Elsevier, vol. 7(1), pages 117-128.
    9. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, 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. Heather Keathley-Herring & Eileen Van Aken & Fernando Gonzalez-Aleu & Fernando Deschamps & Geert Letens & Pablo Cardenas Orlandini, 2016. "Assessing the maturity of a research area: bibliometric review and proposed framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 927-951, November.
    2. Xiuxiu Li & Mingyang Wang & Xu Liu, 2024. "Predicting collaborative relationship among scholars by integrating scholars’ content-based and structure-based features," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3225-3244, June.
    3. Sun, Xiaoling & Ding, Kun & Lin, Yuan, 2016. "Mapping the evolution of scientific fields based on cross-field authors," Journal of Informetrics, Elsevier, vol. 10(3), pages 750-761.
    4. 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.

    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. Sergi Lozano & Xosé-Pedro Rodríguez & Alex Arenas, 2014. "Atapuerca: evolution of scientific collaboration in an emergent large-scale research infrastructure," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1505-1520, February.
    3. Yan, Erjia & Guns, Raf, 2014. "Predicting and recommending collaborations: An author-, institution-, and country-level analysis," Journal of Informetrics, Elsevier, vol. 8(2), pages 295-309.
    4. Jiancheng Guan & Lanxin Pang, 2018. "Bidirectional relationship between network position and knowledge creation in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 201-222, April.
    5. Li, Yongli & Wu, Chong & Wang, Xiaoyu & Luo, Peng, 2014. "A network-based and multi-parameter model for finding influential authors," Journal of Informetrics, Elsevier, vol. 8(3), pages 791-799.
    6. Sameer Kumar & Jariah Mohd. Jan, 2014. "Research collaboration networks of two OIC nations: comparative study between Turkey and Malaysia in the field of ‘Energy Fuels’, 2009–2011," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 387-414, January.
    7. Cimenler, Oguz & Reeves, Kingsley A. & Skvoretz, John, 2014. "A regression analysis of researchers’ social network metrics on their citation performance in a college of engineering," Journal of Informetrics, Elsevier, vol. 8(3), pages 667-682.
    8. Sherkat, Ehsan & Rahgozar, Maseud & Asadpour, Masoud, 2015. "Structural link prediction based on ant colony approach in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 80-94.
    9. Chungmok Lee & Minh Pham & Myong K. Jeong & Dohyun Kim & Dennis K. J. Lin & Wanpracha Art Chavalitwongse, 2015. "A Network Structural Approach to the Link Prediction Problem," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 249-267, May.
    10. Kumar, Ajay & Mishra, Shivansh & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction in complex networks based on Significance of Higher-Order Path Index (SHOPI)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    11. Noémi Gaskó & Rodica Ioana Lung & Mihai Alexandru Suciu, 2016. "A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 613-632, August.
    12. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    13. Yao Hongxing & Lu Yunxia, 2017. "Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method," Journal of Systems Science and Information, De Gruyter, vol. 5(5), pages 446-461, October.
    14. Peng Liu & Haoxiang Xia, 2015. "Structure and evolution of co-authorship network in an interdisciplinary research field," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 101-134, April.
    15. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    16. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    17. Peng Liu & Liang Gui & Huirong Wang & Muhammad Riaz, 2022. "A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    18. Krzysztof Klincewicz, 2016. "The emergent dynamics of a technological research topic: the case of graphene," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 319-345, January.
    19. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
    20. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Predicting the evolution of complex networks via similarity dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 662-672.

    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:104:y:2015:i:1:d:10.1007_s11192-015-1597-3. 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.