A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will myh-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’ futureh-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 futureh-indices. By leveraging relevant factors, we can predict an author'sh-index in five years with an$R^2$value of 0.92 and whether a previously (newly) published paper will contribute to this futureh-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 informedh-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.”
Stored only when both papers are in the corpus — 82 citations exist upstream.