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 optimization, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.
Highlights
The Gospel of St Matthew states: ‘For to all those who have, more will be given’ (Matthew 25:29)
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
Interested readers will find further details on how it is possible to improve the measurement of preferential attachment if one is in possession of exceptionally detailed data in [81], while here we proceed with the review of the Matthew effect in empirical data that stem from an impressive array of different systems
Summary
The Gospel of St Matthew states: ‘For to all those who have, more will be given’ (Matthew 25:29). An illustration of the Matthew effect is presented in figure 1 Already in their seminal work, Barabasi & Albert [16] noted that preferential attachment ought to be readily detected in time-resolved data cataloguing network growth. A node that acquires more connections than another one will increase its connectivity at a higher rate, and an initial difference in the connectivity between two nodes will increase further as the network grows, while the degree of individual nodes will grow proportional with the square root of time This reasoning relates to the so-called first-mover advantage, which has been found accountable for the remarkable marketing success of certain ahead-of-time products [53], as well as the popular acclaim of forefront scientific research despite the fact that it is often less-thorough than follow-up studies [54]. We will review observations of the Matthew effect in empirical data thoroughly in the subsequent sections, but first we survey the methodology that is commonly employed for measuring preferential attachment
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