Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Citation Network Applications in a Scientific Co-authorship Recommender System

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13217))

  • 424 Accesses

Abstract

The problem of co-authors selection in the area of scientific collaborations might be a daunting one. In this paper, we propose a new pipeline that effectively utilizes citation data in the link prediction task on the co-authorship network. In particular, we explore the capabilities of a recommender system based on data aggregation strategies on different graphs. Since graph neural networks proved their efficiency on a wide range of tasks related to recommendation systems, we leverage them as a relevant method for the forecasting of potential collaborations in the scientific community.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alinani, K., Wang, G., Alinani, A., Narejo, D.H.: Who should be my co-author? recommender system to suggest a list of collaborators. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 1427–1433. IEEE (2017)

    Google Scholar 

  2. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  3. Chuan, P.M., Son, L.H., Ali, M., Khang, T.D., Huong, L.T., Dey, N.: Link prediction in co-authorship networks based on hybrid content similarity metric. Appl. Intell. 48(8), 2470–2486 (2017). https://doi.org/10.1007/s10489-017-1086-x

    Article  Google Scholar 

  4. Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

    Google Scholar 

  5. Gupta, C., Jain, Y., De, A., Chakrabarti, S.: Integrating transductive and inductive embeddings improves link prediction accuracy. arXiv preprint arXiv:2108.10108 (2021)

  6. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)

    Google Scholar 

  7. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discovery Data 1 (2006)

    Google Scholar 

  8. Liu, W., Lü, L.: Link prediction based on local random walk. EPL (Europhys. Lett.) 89(5), 58007 (2010)

    Article  Google Scholar 

  9. Liu, Z., Zhang, Q.M., Lü, L., Zhou, T.: Link prediction in complex networks: a local naïve bayes model. EPL (Europhys. Lett.) 96(4), 48007 (2011)

    Article  Google Scholar 

  10. Makarov, I., Bulanov, O., Gerasimova, O., Meshcheryakova, N., Karpov, I., Zhukov, L.E.: Scientific matchmaker: collaborator recommender system. In: van der Aalst, W.M.P., et al. (eds.) AIST 2017. LNCS, vol. 10716, pp. 404–410. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73013-4_37

    Chapter  Google Scholar 

  11. Makarov, I., Bulanov, O., Zhukov, L.E.: Co-author recommender system. In: Kalyagin, V.A., Nikolaev, A.I., Pardalos, P.M., Prokopyev, O.A. (eds.) NET 2016. SPMS, vol. 197, pp. 251–257. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56829-4_18

    Chapter  Google Scholar 

  12. Makarov, I., Gerasimova, O., Sulimov, P., Korovina, K., Zhukov, L.E.: Joint node-edge network embedding for link prediction. In: van der Aalst, W.M.P., et al. (eds.) Analysis of Images, Social Networks and Texts, pp. 20–31. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  13. Sie, R.L., Drachsler, H., Bitter-Rijpkema, M., Sloep, P.: To whom and why should i connect? co-author recommendation based on powerful and similar peers. Int. J. Technol. Enhanced Learn. 4(1–2), 121–137 (2012)

    Article  Google Scholar 

  14. Singh, A., et al.: Edge proposal sets for link prediction. arXiv preprint arXiv:2106.15810 (2021)

  15. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983. KDD 2018, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219890

  16. Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Min. Knowl. Disc. 33(6), 1953–1980 (2019). https://doi.org/10.1007/s10618-019-00650-2

    Article  MATH  Google Scholar 

  17. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. Adv. Neural. Inf. Process. Syst. 31, 5165–5175 (2018)

    Google Scholar 

Download references

Acknowledgements

We acknowledge fruitful discussions with Natalia Semenova.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vadim Porvatov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tishin, V., Sosedka, A., Ibragimov, P., Porvatov, V. (2022). Citation Network Applications in a Scientific Co-authorship Recommender System. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16500-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16499-6

  • Online ISBN: 978-3-031-16500-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics