# Quantifying Long-Term Scientific Impact

arXiv: https://arxiv.org/abs/1306.3293 · HTML: https://mlsanity.com/paper/1306.3293/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2013.

## Built on by (most cited first, 29 of 29)

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- [H2CGL: Modeling Dynamics of Citation Network for Impact Prediction](https://mlsanity.com/paper/2305.01572/) (2023)
- [A Measure of Research Taste](https://mlsanity.com/paper/2105.08089/) (2021)
- [CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2308.04961/) (2023)
- [Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement](https://mlsanity.com/paper/2311.09262/) (2023)
- [Is Peer-Reviewing Worth the Effort?](https://mlsanity.com/paper/2412.14351/) (2024)
- [SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction](https://mlsanity.com/paper/2604.17141/) (2026)
- [ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact](https://mlsanity.com/paper/2606.24892/) (2026)
- [Can Large Language Models Forecast What Researchers Study Next?](https://mlsanity.com/paper/2609.00747/) (2026)
- [Quantifying the Online Long-Term Interest in Research](https://mlsanity.com/paper/2209.06212/) (2022)
- [A multiple k-means cluster ensemble framework for clustering citation trajectories](https://mlsanity.com/paper/2309.04949/) (2023)
- [BioMedJImpact: A Comprehensive Dataset and LLM Pipeline for AI Engagement and Scientific Impact Analysis of Biomedical Journals](https://mlsanity.com/paper/2511.12821/) (2025)
- [LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape](https://mlsanity.com/paper/2604.02600/) (2026)
- [Using Full-Text Content to Characterize and Identify Best Seller Books](https://mlsanity.com/paper/2210.02334/) (2022)
- [Hidden Citations Obscure True Impact in Science](https://mlsanity.com/paper/2310.16181/) (2023)
- [Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model](https://mlsanity.com/paper/2403.00032/) (2024)
- [Learning the mechanisms of network growth](https://mlsanity.com/paper/2404.00793/) (2024)
- [On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction](https://mlsanity.com/paper/2409.16623/) (2024)
- [A Causal Inference Approach for Quantifying Research Impact](https://mlsanity.com/paper/2503.13485/) (2025)
- [Emergent evaluation hubs in a decentralizing large language model ecosystem](https://mlsanity.com/paper/2510.01286/) (2025)
- [Deep versus Broad Technology Search and the Timing of Innovation Impact](https://mlsanity.com/paper/2601.00871/) (2025)
- [FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution](https://mlsanity.com/paper/2605.07208/) (2026)
- [MIRAI: Prediction and Generation of High-Impact Academic Research](https://mlsanity.com/paper/2606.05443/) (2026)
- [GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement](https://mlsanity.com/paper/2609.26361/) (2026)
- [LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction](https://mlsanity.com/paper/2610.10138/) (2026)

## Abstract (arXiv)

The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.