Academic influence has been traditionally measured by citation counts and metrics derived from it, such as H-index and G-index. PageRank based algorithms have been used to give higher weight to citations from more influential papers. A better metric is to add authors into the citation network so that the importance of authors and papers are evaluated recursively within the same framework. Based on such heterogeneous author-citation academic network, this paper gives a new algorithm for ranking authors. It is tested on two large networks, one in Heath domain that contains about 500 million citation links, the other in Computer Science that contains 8 million links. We find that our method outperforms other 10 methods in terms of the number of award winners identified in their top-k rankings. Surprisingly, our method can identify 8 Turing award winners among top 20 authors. It also demonstrates some interesting phenomenons. For instance, among the top authors, our ranking negatively correlates with citation ranking and paper count ranking.
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Amjad, T., & Daud, A. (2017). Indexing of authors according to their domain of expertise. Malaysian Journal of Library and Information Science, 22, 69–82.
Amjad, T., Daud, A., & Akram, A. (2015a). Mutual influence based ranking of authors. Mehran University Research Journal of Engineering and Technology., 34, 103–112.
Amjad, T., Daud, A., & Aljohani, N. R. (2018). Ranking authors in academic social networks: A survey. Library Hi Tech, 36(1), 97–128.
Amjad, T., Ding, Y., Daud, A., Xu, J., & Malic, V. (2015b). Topic-based heterogeneous rank. Scientometrics, 104(1), 313–334.
Bergstrom, C. T., West, J. D., & Wiseman, M. A. (2008). The EigenfactorTM Metrics. Journal of Neuroscience, 28(45), 11433–11434.
Bibi, F., Khan, H., Iqbal, T., Farooq, M., Mehmood, I., & Nam, Y. (2018). Ranking authors in an academic network using social network measures. Applied Sciences, 8(10), 1824.
Bollen, J., Rodriquez, M. A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69(3), 669–687.
Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113–120.
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.
Chen, P., Xie, H., Maslov, S., & Redner, S. (2007). Finding scientific gems with Google’s PageRank algorithm. Journal of Informetrics, 1(1), 8–15.
Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661–703.
Daud, A., Aljohani, N. R., Abbasi, R. A., Rafique, Z., Amjad, T., Dawood, H., & Alyoubi, K. H. (2017). Finding rising stars in co-author networks via weighted mutual influence. In Proceedings of the 26th international conference on World Wide Web companion, international World Wide Web conferences steering committee (pp. 33–41).
Dellavalle, R. P., Schilling, L. M., Rodriguez, M. A., Van de Sompel, H., & Bollen, J. (2007). Refining dermatology journal impact factors using PageRank. Journal of the American Academy of Dermatology, 57(1), 116–119.
Ding, Y., Yan, E., Frazho, A., & Caverlee, J. (2009). PageRank for ranking authors in co-citation networks. Journal of the American Society for Information Science and Technology, 60(11), 2229–2243.
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.
Fragkiadaki, E., & Evangelidis, G. (2016). Three novel indirect indicators for the assessment of papers and authors based on generations of citations. Scientometrics, 106(2), 657–694.
Gao, C., Wang, Z., Li, X., Zhang, Z., & Zeng, W. (2016). PR-index: Using the H-index and pagerank for determining true impact. PloS One, 11(9), e0161755.
González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). A new approach to the metric of journals’ scientific prestige: The SJR indicator. Journal of Informetrics, 4(3), 379–391.
Gross, P. L. K., & Gross, E. M. (1927). College libraries and chemical education. Science, 66(1713), 385–389.
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143(1), 29–36.
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
Liang, R., & Jiang, X. (2016). Scientific ranking over heterogeneous academic hypernetwork. In Proceedings of the Thirtieth AAAI conference on artificial intelligence, AAAI’16 (pp. 20–26). AAAI Press.
Lindsey, D. (1982). Further evidence for adjusting for multiple authorship. Scientometrics, 4(5), 389–395.
Liu, N. C., & Cheng, Y. (2005). The academic ranking of World Universities. Higher Education in Europe, 30(2), 127–136.
Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41(6), 1462–1480.
Ma, N., Guan, J., & Zhao, Y. (2008). Bringing PageRank to the citation analysis. Information Processing and Management, 44(2), 800–810.
Radicchi, F., Fortunato, S., Markines, B., & Vespignani, A. (2009). Diffusion of scientific credits and the ranking of scientists. Physical Review E, 80(5), 056103.
Sayyadi, H., & Getoor, L. (2009). FutureRank: Ranking scientific articles by predicting their future PageRank. In C. Apte, H. Park, K. Wang, M. J. Zaki (Eds.), Proceedings of the 2009 SIAM international conference on data mining, society for industrial and applied mathematics, (pp. 533–544). Philadelphia, PA, https://doi.org/10.1137/1.9781611972795.46.
Sidiropoulos, A., & Manolopoulos, Y. (2006). Generalized comparison of graph-based ranking algorithms for publications and authors. Journal of Systems and Software, 79(12), 1679–1700.
Steinbrüchel, C. (2018). A citation index for principal investigators. Scientometrics. https://doi.org/10.1007/s11192-018-2971-8.
Su, C., Pan, Y., Zhen, Y., Ma, Z., Yuan, J., Guo, H., et al. (2011). PrestigeRank: A new evaluation method for papers and journals. Journal of Informetrics, 5(1), 1–13.
Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., & Wu, T. (2009). RankClus: Integrating clustering with ranking for heterogeneous information network analysis. In Proceedings of the 12th international conference on extending database technology advances in database technology - EDBT ’09 (p. 565). ACM Press, Saint Petersburg, Russia.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). ArnetMiner: Extraction and mining of academic social networks. In Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining - KDD 08 (p. 990). ACM Press, Las Vegas, Nevada, USA.
Usmani, A., & Daud, A. (2017). Unified author ranking based on integrated publication and venue rank. International Arab Journal of Information Technology (IAJIT), 14(1), 111–117.
Walker, D., Xie, H., Yan, K. K., & Maslov, S. (2007). Ranking scientific publications using a model of network traffic. Journal of Statistical Mechanics: Theory and Experiment, 06, P06010–P06010.
Wang, Y., Tong, Y., & Zeng, M. (2013). Ranking scientific articles by exploiting citations, authors, journals, and time information. In Proceedings of the twenty-seventh AAAI conference on artificial intelligence, AAAI’13 (pp. 933–939). AAAI Press.
West, J. D., Jensen, M. C., Dandrea, R. J., Gordon, G. J., & Bergstrom, C. T. (2013). Author-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community. Journal of the American Society for Information Science and Technology, 64(4), 787–801.
Yan, E. (2014). Topic-based Pagerank: Toward a topic-level scientific evaluation. Scientometrics, 100(2), 407–437.
Yan, E., & Ding, Y. (2011). Discovering author impact: A PageRank perspective. Information Processing and Management, 47(1), 125–134.
Zhou, D., Orshanskiy, S. A., Zha, H., & Giles, C. L. (2007). Co-ranking authors and documents in a heterogeneous network. In Seventh IEEE international conference on data mining (ICDM 2007) (pp. 739–744). IEEE, Omaha, NE, USA.
Zhou, J., Zeng, A., Fan, Y., & Di, Z. (2016). Ranking scientific publications with similarity-preferential mechanism. Scientometrics, 106(2), 805–816.
The research is supported by NSERC Discovery Grant.
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Zhao, F., Zhang, Y., Lu, J. et al. Measuring academic influence using heterogeneous author-citation networks. Scientometrics 118, 1119–1140 (2019). https://doi.org/10.1007/s11192-019-03010-5
- Heterogeneous network
- Author ranking
- Scholarly data