Modeling the coevolution between citations and coauthorships in scientific papers

TL;DR

This paper introduces a geometric model to simulate the coevolution of citations and coauthorships in scientific research, validated with PNAS data, revealing insights into distribution patterns and author behaviors.

Contribution

It presents a novel geometric graph model that captures the interactive dynamics of citations and coauthorships, explaining observed distribution shapes and correlations.

Findings

The model reproduces citation distribution shapes, including Poisson and power-law.

It explains how author decisions influence distribution emergence.

The model captures positive correlations between papers, citations, and collaborators.

Abstract

Collaborations and citations within scientific research grow simultaneously and interact dynamically. Modelling the coevolution between them helps to study many phenomena that can be approached only through combining citation and coauthorship data. A geometric graph for the coevolution is proposed, the mechanism of which synthetically expresses the interactive impacts of authors and papers in a geometrical way. The model is validated against a data set of papers published on PNAS during 2007-2015. The validation shows the ability to reproduce a range of features observed with citation and coauthorship data combined and separately. Particularly, in the empirical distribution of citations per author there exist two limits, in which the distribution appears as a generalized Poisson and a power-law respectively. Our model successfully reproduces the shape of the distribution, and provides an explanation for how the shape emerges via the decisions of authors. The model also captures the empirically positive correlations between the numbers of authors' papers, citations and collaborators.