Measuring academic influence using heterogeneous author-citation networks

Abstract

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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Notes

  1. 1.

    https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/.

  2. 2.

    http://zhao15m.myweb.cs.uwindsor.ca/apr/.

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Acknowledgements

The research is supported by NSERC Discovery Grant.

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Correspondence to Fen Zhao.

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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

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Keywords

  • Heterogeneous network
  • Author ranking
  • PageRank
  • Scholarly data