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Machine Learning Based Approach for Future Prediction of Authors in Research Academics

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Abstract

In the present time, it is important to predict superior authors from the huge research community, which determines their performance with their future prospects and opportunities, in the field of scientific research. There is a need to solve the authors’ future prediction problem for equally assessing the performance of the researchers that helps to improve research quality highly, influence other types of research, that will also help to identify the research carrier and other research parameters, that will further influence budding researchers. In our proposed model, to solve the authors’ future prediction problems, five machines learning models i.e., SVM, Logistic Regression, Naive Bayes, Decision Tree, and Random Forest are implemented on a data set of 2750 authors where 1000 authors are the ranked authors. So, we have collected 2750 authors’ data from their bibliographic data sets. Then used feature engineering and feature scaling to build the desired model and prepare the final data set used to build the model. There after varied data sizes and training ratios against the five ML models and measured all the evaluation metrics in different cases. We have seen that the Decision Tree and Random Forest model outperform the other models with an accuracy score of 1.0 and the other scores also performed well.

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Notes

  1. https://www.adscientificindex.com/top-100-scientist/.

  2. https://lfs.aminer.cn/lab-datasets/citation/acm.v9.zip.

References

  1. Ding Y, Yan E, Frazho A, Caverlee J. Pagerank for ranking authors in co-citation networks. J Am Soc Inf Sci Technol. 2009;60(11):2229–43.

    Article  Google Scholar 

  2. Zhao P, Han J, Sun Y. P-rank: a comprehensive structural similarity measure over information networks. In: Proceedings of the 18th ACM conference on information and knowledge management. 2009; p. 553–562.

  3. Gollapalli SD, Mitra P, Giles CL. Ranking authors in digital libraries. In: Proceedings of the 11th annual international ACM/IEEE joint conference on digital libraries. 2011; p. 251–254.

  4. Senanayake U, Piraveenan M, Zomaya AY. The p-index: ranking scientists using network dynamics. Procedia Comput Sci. 2014;29:465–77.

    Article  Google Scholar 

  5. Liu Z, Huang H, Wei X, Mao X. Tri-rank: an authority ranking framework in heterogeneous academic networks by mutual reinforce. In: 2014 IEEE 26th international conference on tools with artificial intelligence. IEEE; 2014. p. 493–500.

  6. Pradhan D, Paul PS, Maheswari U, Nandi S, Chakraborty T. C3-index: revisiting author’s performance measure. In: Proceedings of the 8th ACM conference on web science. 2016; p. 318–319.

  7. Franceschet M, Colavizza G. Timerank: a dynamic approach to rate scholars using citations. J Informetr. 2017;11(4):1128–41.

    Article  Google Scholar 

  8. Zhang J, Ning Z, Bai X, Kong X, Zhou J, Xia F. Exploring time factors in measuring the scientific impact of scholars. Scientometrics. 2017;112(3):1301–21.

    Article  Google Scholar 

  9. Zhang J, Hu Y, Ning Z, Tolba A, Elashkar E, Xia F. AIRank: author impact ranking through positions in collaboration networks. Complexity. 2018;2018:4697485.

    Google Scholar 

  10. Zhang C, Yu L, Zhang X, Chawla NV. Task-guided and semantic-aware ranking for academic author-paper correlation inference. International Joint Conferences on Artificial Intelligence. 2018.

  11. Bornmann L, Leydesdorff L, Wang J. How to improve the prediction based on citation impact percentiles for years shortly after the publication date? J Informetr. 2014;8(1):175–80.

    Article  Google Scholar 

  12. Varlamis PGTG. I detecting rising stars in dynamic collaborative networks. J Informetr. 2017;11(1):198.

    Article  Google Scholar 

  13. Daud A, Aljohani NR, Abbasi RA, Rafique Z, Amjad T, Dawood H, Alyoubi KH. Finding rising stars in co-author networks via weighted mutual influence. In: Proceedings of the 26th international conference on world wide web companion. 2017; p. 33–41.

  14. Amjad T, Daud A, Aljohani NR. Ranking authors in academic social networks: a survey. Library Hi Tech. 2018.

  15. Zhu L, Zhu D, Wang X, Cunningham SW, Wang Z. An integrated solution for detecting rising technology stars in co-inventor networks. Scientometrics. 2019;121(1):137–72.

    Article  Google Scholar 

  16. Zhang F, Bai X, Lee I. Author impact: evaluations, predictions, and challenges. IEEE Access. 2019;7:38657–69.

    Article  Google Scholar 

  17. Wang M, Zhang J, Jiao S, Zhang T. Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations. PLoS One. 2019;14(11):0225276.

    Article  Google Scholar 

  18. Daud A, Song M, Hayat MK, Amjad T, Abbasi RA, Dawood H, Ghani A. Finding rising stars in bibliometric networks. Scientometrics. 2020;124(1):633–61.

    Article  Google Scholar 

  19. Amjad T, Munir J. Investigating the impact of collaboration with authority authors: a case study of bibliographic data in field of philosophy. Scientometrics. 2021;126(5):4333–53.

    Article  Google Scholar 

  20. Xiao S, Yan J, Li C, Jin B, Wang X, Yang X, Chu SM, Zha H. On modeling and predicting individual paper citation count over time. In: Ijcai. 2016; p. 2676–2682.

  21. Ashraf S, Iqbal HR, Nawab RMA. Cross-genre author profile prediction using stylometry-based approach. In: CLEF (Working Notes). Citeseer; 2016. p. 992–999.

  22. Reddy TR, Vardhan BV, Reddy PV. Author profile prediction using pivoted unique term normalization. Indian J Sci Technol. 2016; 9(46). https://doi.org/10.17485/ijst/2016/v9i46/99404.

  23. Nie Y, Zhu Y, Lin Q, Zhang S, Shi P, Niu Z. Academic rising star prediction via scholar’s evaluation model and machine learning techniques. Scientometrics. 2019;120(2):461–76.

    Article  Google Scholar 

  24. Bin-Obaidellah O, Al-Fagih AE. Scientometric indicators and machine learning-based models for predicting rising stars in academia. In: 2019 7th international conference on smart computing & communications (ICSCC). IEEE; 2019. p. 1–7.

  25. Bütün E, Kaya M. Predicting citation count of scientists as a link prediction problem. IEEE Trans Cybern. 2019;50(10):4518–29.

    Article  Google Scholar 

  26. Shoaib M, Daud A, Amjad T. Author name disambiguation in bibliographic databases: a survey. arXiv preprint arXiv:2004.06391. 2020.

  27. Su Z. Prediction of future citation count with machine learning and neural network. In: 2020 Asia-Pacific conference on image processing, electronics and computers (IPEC). IEEE; 2020. p. 101–104.

  28. Fujita M, Inoue H, Terano T. Analyzing promising researchers using network centralities of co-authorship networks from academic literature. New Gener Comput. 2021;39(1):181–97.

    Article  Google Scholar 

  29. Khan ZY, Niu Z, Sandiwarno S, Prince R. Deep learning techniques for rating prediction: a survey of the state-of-the-art. Artif Intell Rev. 2021;54(1):95–135.

    Article  Google Scholar 

  30. Daud A, ul Islam N, Li X, Razzak I, Hayat MK. Identifying rising stars via supervised machine learning. IEEE Trans Comput Soc Syst. 2022. https://doi.org/10.1109/TCSS.2022.3178070.

  31. Ávila-Toscano JH, Romero-Pérez I, Saavedra-Guajardo E, Marenco-Escuderos A. Determinants of colombian scientific production in social sciences articles indexed in wos, scopus and other databases: tree of classification and regression. Rev Interam Bibliotecol. 2022;45(1). https://doi.org/10.17533/udea.rib.v45n1e339712.

  32. Bhattacharya S. Discoveries of research genealogy from large-scale academic dataset: issues, challenges and application. Int J Comput Sci Eng. 2019;07:262–7.

    Google Scholar 

  33. Wang X, van Harmelen F, Huang Z. Recommending scientific datasets using author networks in ensemble methods. 2022.

  34. Nawaz A, Malik M. Rising stars prediction in reviewer network. Electron Commer Res. 2022;22(1):53–75.

    Article  Google Scholar 

  35. Hirsch JE. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci. 2005;102(46):16569–72.

    Article  MATH  Google Scholar 

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Author notes

  1. Sovan Bhattacharya, Ayan Banerjee, Abhik Goswami, Subrata Nandi and Dinesh Kumar Pradhan contributed equally to this work.

Authors and Affiliations

  1. Department of CSE, Dr. B. C. Roy Engineering College, Jemua Road, Durgapur, West Bengal, 713206, India

    Sovan Bhattacharya, Ayan Banerjee, Abhik Goswami & Dinesh Kumar Pradhan

  2. Department of CSE, National Institute of Technology, Mahatma Gandhi Road, Durgapur, West Bengal, 713209, India

    Sovan Bhattacharya & Subrata Nandi

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Correspondence to Sovan Bhattacharya.

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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.

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Bhattacharya, S., Banerjee, A., Goswami, A. et al. Machine Learning Based Approach for Future Prediction of Authors in Research Academics. SN COMPUT. SCI. 4, 306 (2023). https://doi.org/10.1007/s42979-023-01692-6

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