Abstract
A widely used measure of scientific impact is citations. However, due to their power-law distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h-index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors’ future h-indices based on their current scientific impact. Second, we examine the factors that drive papers—either previously or newly published—to increase their authors’ predicted future h-indices. By leveraging relevant factors, we can predict an author’s h-index in five years with an \(R^2\) value of 0.92 and whether a previously (newly) published paper will contribute to this future h-index with an \(F_1\) score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h-index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants.”
Y. Dong and R.A. Johnson—Provided equal contribution to this work.
This work was published at the 8th ACM International Conference on Web Search and Data Mining (WSDM’15) [1]. This extended abstract has been largely extracted from the publication.
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Dong, Y., Johnson, R.A., Chawla, N.V.: Will this paper increase your h-index?: Scientific impact prediction. In: WSDM 2015, pp. 149–158. ACM, New York (2015)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. PNAS 102(46), 16569–16572 (2005)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: Extraction and mining of academic social networks. In: KDD 2008, pp. 990–998 (2008)
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© 2015 Springer International Publishing Switzerland
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Dong, Y., Johnson, R.A., Chawla, N.V. (2015). Will This Paper Increase Your h-index?. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_26
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DOI: https://doi.org/10.1007/978-3-319-23461-8_26
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