The Matthew effect in empirical data

TL;DR

This paper reviews the concept of the Matthew effect across various domains, discussing how advantage accumulates in social, scientific, and natural systems, and explores methods to measure and understand this phenomenon.

Contribution

It provides a comprehensive review of methodologies for measuring preferential attachment and examines the presence of the Matthew effect in diverse empirical data sets.

Findings

Matthew effect observed in scientific collaboration and citation networks

Power-law and scaling behaviors linked to cumulative advantage

Discussion on whether the effect arises from chance or optimization

Abstract

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.