Heavy-Tailed Distribution of the Number of Papers within Scientific Journals

TL;DR

This paper analyzes the distribution of scientific publications per author, revealing a heavy-tailed but sub-power-law pattern, modeled through a modified preferential attachment process considering scientists' finite careers.

Contribution

It introduces a statistical model explaining the publication distribution with a modified preferential attachment process accounting for career span.

Findings

Distribution of papers per author is heavy-tailed but lighter than a power law.

Modified preferential attachment model fits the observed distribution.

Analytical and numerical validation of the model.

Abstract

Scholarly publications represent at least two benefits for the study of the scientific community as a social group. First, they attest of some form of relation between scientists (collaborations, mentoring, heritage,...), useful to determine and analyze social subgroups. Second, most of them are recorded in large data bases, easily accessible and including a lot of pertinent information, easing the quantitative and qualitative study of the scientific community. Understanding the underlying dynamics driving the creation of knowledge in general, and of scientific publication in particular can contribute to maintaining a high level of research, by identifying good and bad practices in science. In this article, we aim at advancing this understanding by a statistical analysis of publication within peer-reviewed journals. Namely, we show that the distribution of the number of papers published by an author in a given journal is heavy-tailed, but has lighter tail than a power law. Interestingly, we demonstrate (both analytically and numerically) that such distributions match the result of an modified preferential attachment process, where, on top of a Barab\'asi-Albert process, we take finite career span of scientists into account.