Work

Distortive effects of initial‐based name disambiguation on measurements of large‐scale coauthorship networks

Year: 2015

Type: article

Abstract: Scholars have often relied on name initials to resolve name ambiguities in large‐scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial‐based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests that assumption by analyzing coauthorship networks from five academic fields—biology, computer science, nanoscience, neuroscience, and physics—and an interdisciplinary journal, PNAS . Name instances in data sets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions. We use disambiguated data as a proxy of ground‐truth to test the performance of three types of initial‐based disambiguation. Our results show that initial‐based disambiguation can misrepresent statistical properties of coauthorship networks: It deflates the number of unique authors, number of components, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size. Also, on average, more than half of top 10 productive or collaborative authors drop off the lists. A sian names were found to account for the majority of misidentification by initial‐based disambiguation due to their common surname and given name initials.

Source: Journal of the Association for Information Science and Technology

DOI: 10.1002/asi.23489

Authors: Jinseok Kim, Jana Diesner

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: Ford Foundation

Award: G2551378409