Will This Paper Increase Your h-index? Scientific Impact Prediction
Not on the map. Why?
Abstract (arXiv)
Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Dec 2014.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2023Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglementcited by 2
- 2026SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Predictioncited by 2
- 2021Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnologycited by 0
- 2024Hierarchical Information Enhancement Network for Cascade Prediction in Social Networkscited by 0
- 2024On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Predictioncited by 0
- 2025A Causal Inference Approach for Quantifying Research Impactcited by 0
- 2026SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction18 Apr 2026
- 2025A Causal Inference Approach for Quantifying Research Impact7 Mar 2025
- 2024On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction25 Sep 2024
- 2024Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks22 Mar 2024
- 2023Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement15 Nov 2023
- 2021Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology3 Jun 2021