🤖 AI Summary
Author name ambiguity often leads to erroneous merging or splitting of nodes in co-authorship networks, distorting network topology and compromising analytical reliability. This study systematically quantifies, for the first time, the systematic biases introduced by commonly used initial-based heuristic disambiguation methods across a range of network metrics. By constructing a high-precision benchmark network and employing counterfactual analysis combined with stochastic perturbations to simulate varying levels of disambiguation error, the authors demonstrate that initial-based disambiguation substantially underestimates network size, overestimates connectivity and collaboration intensity among authors, and obscures the true fragmentation of research communities. These findings reveal the potentially misleading impact of prevailing disambiguation practices on the analysis of scientific collaboration networks.
📝 Abstract
Researchers analyze coauthorship networks, but author name ambiguity in their network data remains a significant challenge as it can change the number of vertices, distorting network properties. Although many scholars use straightforward heuristics for author name disambiguation using author's forename initials, these techniques can skew our understanding of network properties by merging or splitting vertices, raising concerns about the reliability and validity of these methods. This study investigates how different levels of vertex merging and splitting errors that are induced by name ambiguity impact network measures, using three large coauthorship networks with highly accurate algorithmic author name disambiguation. As a counterfactual scenario, two initial-based disambiguation methods widely used in coauthorship network research were applied to these datasets. Nine coauthorship network metrics were computed while varying randomly the numbers of merged or split vertices. Results show that initial-based disambiguation generates coauthorship networks with specific network properties underestimated, leading to the discovery of coauthorship networks that are smaller and more closely connected than they genuinely are. In contrast, other network metric values increase, making authors appear more collaborative and embedded within less fragmented research communities than they are. The study emphasizes the importance of careful disambiguation of vertex names in analyzing coauthorship networks for rigorous and valid findings.