# The Matthew effect in empirical data

arXiv: https://arxiv.org/abs/1408.5124 · HTML: https://mlsanity.com/paper/1408.5124/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2014.

## Built on by (most cited first, 10 of 10)

- [ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://mlsanity.com/paper/2303.13367/) (2023)
- [Device-Cloud Collaborative Learning for Recommendation](https://mlsanity.com/paper/2104.06624/) (2021)
- [Complex systems approach to natural language](https://mlsanity.com/paper/2401.02772/) (2024)
- [Leveraging LLM-based agents for social science research: insights from citation network simulations](https://mlsanity.com/paper/2511.03758/) (2025)
- [Causal Lifting and Link Prediction](https://mlsanity.com/paper/2302.01198/) (2023)
- [Scholar Inbox: Personalized Paper Recommendations for Scientists](https://mlsanity.com/paper/2504.08385/) (2025)
- [Quantifying the Global Support Network for Non-State Armed Groups (NAGs)](https://mlsanity.com/paper/2102.00564/) (2021)
- [Weak Supervision for Improved Precision in Search Systems](https://mlsanity.com/paper/2503.07025/) (2025)
- [A Multi-View Consistency Framework with Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2601.19266/) (2026)
- [Whose fairness? Structural concentration in AI bias research](https://mlsanity.com/paper/2607.05574/) (2026)

## Abstract (arXiv)

The Matthew effect describes the phenomenon that in societies the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimisation, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.
