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A systematic survey on collaborator finding systems in scientific social networks

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Abstract

The increasing number of researchers and scientists participating in online communities has induced big challenges for users who are looking for researchers who are interested. As a result, finding potential collaborators among the huge amount of online information is going to be even much more important in the future. Collaborator recommendation is a kind of expert recommendation in scientific fields. A number of published papers have proposed new algorithms for an expert or a collaborator finding and tacking a narrower point of view. For instance, some of these papers have particularly considered a collaborator finding problem. New scientific social networks, such as ResearchGate, Academia, Mendeley, and so on, have provided some facilities to their users for finding new collaborators. In this paper, first of all, we review proposed models for an expert and a collaborator finding in scientific and academic social networks in a systematic manner. Next, collaborator finding facilities in online scientific social networks are evaluated. Finally, the defects and open challenges of the models are looked into and some propositions for the future works are presented.

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Notes

  1. Although a bit different, hereafter we will use academic and scientific words interchangeably in this paper.

  2. https://dblp.uni-trier.de/.

  3. http://keg.cs.tsinghua.edu.cn/project/PSN/dataset.html.

  4. www.citeulike.org.

  5. http://ilk.uvt.nl/.

  6. Kasetsart University Research Development Institute.

  7. http://www.informatik.uni-trier.de/_ley/db.

  8. http://academic.research.microsoft.com/

  9. https://archive.org/details/stackexchange.

  10. http://dblp.uni-trier.de/.

  11. http://wikicfp.com/examples/wikicfp.v1.2008.xml.gz.

  12. https://archive.org/details/stackexchange.

  13. http://stackoverflow.com.

  14. https://www.mendeley.com/.

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  1. Zahra Roozbahani and Jalal Rezaeenour authors have contributed in the paper equally.

Authors and Affiliations

  1. Department of Computer Engineering and IT, University of Qom, Qom, Iran

    Zahra Roozbahani & Amir Jalaly Bidgoly

  2. Department of Industrial Engineering, University of Qom, Qom, Iran

    Jalal Rezaeenour

  3. Department of Computer Science, Yazd University, Yazd, Iran

    Hanif Emamgholizadeh

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  1. Zahra Roozbahani
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Roozbahani, Z., Rezaeenour, J., Emamgholizadeh, H. et al. A systematic survey on collaborator finding systems in scientific social networks. Knowl Inf Syst 62, 3837–3879 (2020). https://doi.org/10.1007/s10115-020-01483-y

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