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Link prediction in co-authorship networks based on hybrid content similarity metric

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Abstract

Link prediction in online social networks is used to determine new interactions among its members which are likely to occur in the future. Link prediction in the co-authorship network has been regarded as one of the main targets in link prediction researches so far. Researchers have focused on analyzing and proposing solutions to give efficient recommendation for authors who can work together in a science project. In order to give precise prediction of links between two ubiquitous authors in a co-authorship network, it is preferable to design a similarity metric between them and then utilizing it to determine the most possible co-author(s). However, the relevant researches did not regard the integration of paper’s content in the metric itself. This is important when considering the collaboration between scientists since it is possible that authors having same research interests are more likely to have a joint paper than those in different researches. In this paper, we propose a new metric for link prediction in the co-authorship network based on the content similarity named as LDAcosin. Mathematical notions of the link prediction in the co-authorship network and a link prediction algorithm based on topic modeling are proposed. The new metric is experimentally validated on the public bibliographic collection.

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Acknowledgments

This research was supported by Center for Research and Applications in Science and Technology, Hung Yen University of Technology and Education, under grant number UTEHY.T026.P1718.02.

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Authors and Affiliations

  1. Hung Yen University of Technology and Education, Hung Yen, Vietnam

    Pham Minh Chuan

  2. VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam

    Le Hoang Son

  3. University of Southern Queensland, Toowoomba, 4300, Australia

    Mumtaz Ali

  4. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

    Pham Minh Chuan, Tran Dinh Khang & Le Thanh Huong

  5. Techno India College of Technology, Kolkata, India

    Nilanjan Dey

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  1. Pham Minh Chuan
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  2. Le Hoang Son
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  5. Le Thanh Huong
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Correspondence to Le Hoang Son.

Appendix

Appendix

Source codes and datasets of the research can be found at this link: https://sourceforge.net/p/affin-ldacosin/code/ci/master/tree/

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Chuan, P.M., Son, L.H., Ali, M. et al. Link prediction in co-authorship networks based on hybrid content similarity metric. Appl Intell 48, 2470–2486 (2018). https://doi.org/10.1007/s10489-017-1086-x

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