IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v8y2014i2p295-309.html
   My bibliography  Save this article

Predicting and recommending collaborations: An author-, institution-, and country-level analysis

Author

Listed:
  • Yan, Erjia
  • Guns, Raf

Abstract

This study examines collaboration dynamics with the goal to predict and recommend collaborations starting from the current topology. Author-, institution-, and country-level collaboration networks are constructed using a ten-year data set on library and information science publications. Different statistical approaches are applied to these collaboration networks. The study shows that, for the employed data set in particular, higher-level collaboration networks (i.e., country-level collaboration networks) tend to yield more accurate prediction outcomes than lower-level ones (i.e., institution- and author-level collaboration networks). Based on the recommended collaborations of the data set, this study finds that neighbor-information-based approaches are more clustered on a 2-D multidimensional scaling map than topology-based ones. Limitations of the applied approaches on sparse collaboration networks are also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:2:p:295-309
    DOI: 10.1016/j.joi.2014.01.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157714000091
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2014.01.008?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. Erjia Yan & Cassidy R. Sugimoto, 2011. "Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(8), pages 1498-1514, August.
    2. Jarno Hoekman & Koen Frenken & Frank Oort, 2009. "The geography of collaborative knowledge production in Europe," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(3), pages 721-738, September.
    3. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    4. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    5. Erjia Yan & Cassidy R. Sugimoto, 2011. "Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(8), pages 1498-1514, August.
    6. Roderik Ponds & Frank Van Oort & Koen Frenken, 2007. "The geographical and institutional proximity of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 86(3), pages 423-443, August.
    7. Guo, Fangjian & Yang, Zimo & Zhou, Tao, 2013. "Predicting link directions via a recursive subgraph-based ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3402-3408.
    8. 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.
    9. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    10. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    11. Hildrun Kretschmer, 2004. "Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the Web," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 409-420, August.
    12. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    13. 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.
    14. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
    15. Dalibor Fiala & François Rousselot & Karel Ježek, 2008. "PageRank for bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 135-158, July.
    16. Birger Larsen & Peter Ingwersen & Berit Lund, 2009. "Data fusion according to the principle of polyrepresentation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(4), pages 646-654, April.
    17. Ron Boschma, 2005. "Proximity and Innovation: A Critical Assessment," Regional Studies, Taylor & Francis Journals, vol. 39(1), pages 61-74.
    18. 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. 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.
    2. Branco Ponomariov & Craig Boardman, 2016. "What is co-authorship?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1939-1963, December.
    3. Tian, Yunpei & Li, Gang & Mao, Jin, 2023. "Predicting the evolution of scientific communities by interpretable machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    4. Yi Zhang & Mengjia Wu & Guangquan Zhang & Jie Lu, 2023. "Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 775-790, July.
    5. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Alhoori, Hamed & Furuta, Richard, 2017. "Recommendation of scholarly venues based on dynamic user interests," Journal of Informetrics, Elsevier, vol. 11(2), pages 553-563.
    7. Lili Wang & Xianwen Wang & Niels J. Philipsen, 2017. "Network structure of scientific collaborations between China and the EU member states," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 765-781, November.
    8. Pan, Xuelian & Yan, Erjia & Wang, Qianqian & Hua, Weina, 2015. "Assessing the impact of software on science: A bootstrapped learning of software entities in full-text papers," Journal of Informetrics, Elsevier, vol. 9(4), pages 860-871.
    9. Jinseok Kim & Jana Diesner, 2019. "Formational bounds of link prediction in collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 687-706, May.
    10. Jiang Li & Yueting Li, 2015. "Patterns and evolution of coauthorship in China’s humanities and social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1997-2010, March.
    11. Lili Yuan & Yanni Hao & Minglu Li & Chunbing Bao & Jianping Li & Dengsheng Wu, 2018. "Who are the international research collaboration partners for China? A novel data perspective based on NSFC grants," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 401-422, July.
    12. 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.
    13. Yan Qi & Xin Zhang & Zhengyin Hu & Bin Xiang & Ran Zhang & Shu Fang, 2022. "Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5519-5550, September.
    14. Ahlgren, Per & Waltman, Ludo, 2014. "The correlation between citation-based and expert-based assessments of publication channels: SNIP and SJR vs. Norwegian quality assessments," Journal of Informetrics, Elsevier, vol. 8(4), pages 985-996.
    15. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    16. Guns, Raf & Wang, Lili, 2017. "Detecting the emergence of new scientific collaboration links in Africa: A comparison of expected and realized collaboration intensities," Journal of Informetrics, Elsevier, vol. 11(3), pages 892-903.
    17. 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.
    18. Yun Liu & Yijie Cheng & Zhe Yan & Xuanting Ye, 2018. "Multilevel Analysis of International Scientific Collaboration Network in the Influenza Virus Vaccine Field: 2006–2013," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    19. Chen Yang & Tingting Liu & Xiaohong Chen & Yiyang Bian & Yuewen Liu, 2020. "HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 429-449, April.
    20. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    21. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.

    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. Yichi Zhang & Zhiliang Dong & Sen Liu & Peixiang Jiang & Cuizhi Zhang & Chao Ding, 2021. "Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries," Sustainability, MDPI, vol. 13(3), pages 1-23, January.
    2. 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.
    3. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Xie, Zheng & Zhang, Shengjun & Yi, Dongyun, 2015. "Predicting link directions using local directed path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 260-267.
    4. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    5. Frenken, Koen & Hardeman, Sjoerd & Hoekman, Jarno, 2009. "Spatial scientometrics: Towards a cumulative research program," Journal of Informetrics, Elsevier, vol. 3(3), pages 222-232.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. Lingling Zhang & Jing Li & Qiuliu Zhang & Fan Meng & Weili Teng, 2019. "Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 311-338, January.
    11. Li, Yongli & Luo, Peng & Fan, Zhi-ping & Chen, Kun & Liu, Jiaguo, 2017. "A utility-based link prediction method in social networks," European Journal of Operational Research, Elsevier, vol. 260(2), pages 693-705.
    12. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    13. Jorge Cerdeira & João Mesquita & Elizabeth S. Vieira, 2023. "International research collaboration: is Africa different? A cross-country panel data analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2145-2174, April.
    14. Wang, Jue & Zhang, Liwei, 2018. "Proximal advantage in knowledge diffusion: The time dimension," Journal of Informetrics, Elsevier, vol. 12(3), pages 858-867.
    15. Cilem Selin Hazir & Corinne Autant-Bernard, 2012. "Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology," Working Papers 1212, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    16. Laurent R. Bergé, 2017. "Network proximity in the geography of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 785-815, November.
    17. Sara Amoroso & Alex Coad & Nicola Grassano, 2017. "European R&D networks: A snapshot from the 7th EU Framework Programme," JRC Working Papers on Corporate R&D and Innovation JRC107546, Joint Research Centre (Seville site).
    18. Kaihuang Zhang & Qinglan Qian & Yijing Zhao, 2020. "Evolution of Guangzhou Biomedical Industry Innovation Network Structure and Its Proximity Mechanism," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    19. Ferretti, Marco & Guerini, Massimiliano & Panetti, Eva & Parmentola, Adele, 2022. "The partner next door? The effect of micro-geographical proximity on intra-cluster inter-organizational relationships," Technovation, Elsevier, vol. 111(C).
    20. Hiroyasu Inoue & Kentaro Nakajima & Yukiko Umeno Saito, 2019. "Localization of collaborations in knowledge creation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(1), pages 119-140, February.

    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:eee:infome:v:8:y:2014:i:2:p:295-309. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.