Coauthorship networks: A directed network approach considering the order and number of coauthors

TL;DR

This paper introduces a directed, weighted network model for coauthorship analysis that incorporates author order and contribution levels, providing deeper insights into collaboration hierarchies and identifying prominent scholars.

Contribution

It presents a novel approach using a coauthorship credit model to account for author order, improving upon traditional undirected network analyses.

Findings

The directed network approach reveals hierarchical collaboration patterns.

The method accurately identifies prominent scholars in the field.

It complements traditional undirected coauthorship metrics.

Abstract

In many scientific fields, the order of coauthors on a paper conveys information about each individual's contribution to a piece of joint work. We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized. This is basically the same as assuming that each co-author has contributed equally to a paper. We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes co-authoring as a directed, weighted, and self-looped network. We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika. Results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars. As another form of validation, we also show how our approach accurately detects prominent scholars in the Psychometric Society affiliated with the journal.