Hyperauthored papers disproportionately amplify important egocentric network metrics

TL;DR

Hyperauthored papers significantly distort co-authorship network metrics, affecting the analysis of scientific collaboration, but applying a threshold cutoff or fractional counting can mitigate these effects.

Contribution

This study introduces a method to determine cutoff thresholds for hyperauthored papers and compares network metrics with and without these papers in genomics.

Findings

Hyperauthored papers alter centrality and network topology.

Threshold cutoff and fractional counting reduce hyperauthorship impact.

Hyperauthorship has minimal effect on overall network cohesion.

Abstract

Hyperauthorship, a phenomenon whereby there are a disproportionately large number of authors on a single paper, is increasingly common in several scientific disciplines, but with unknown consequences for network metrics used to study scientific collaboration. The validity of co-authorship as a proxy for scientific collaboration is affected by this. Using bibliometric data from publications in the field of genomics, we examine the impact of hyperauthorship on metrics of scientific collaboration, and propose a method to determine a suitable cutoff threshold for hyperauthored papers and compare co-authorship networks with and without hyperauthored works. Our analysis reveals that including hyperauthored papers dramatically impacts the structural positioning of central authors and the topological characteristics of the network, while producing small influences on whole-network cohesion measures. We present two solutions to minimize the impact of hyperauthorship: using a mathematically grounded and reproducible calculation of threshold cutoff to exclude hyperauthored papers or fractional counting to weight network results. Our findings affirm the structural influences of hyperauthored papers and suggest that scholars should be mindful when using co-authorship networks to study scientific collaboration.