  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 this assumption by
analyzing coauthorship networks from five academic fields - biology, computer
science, nanoscience, neuroscience, and physics - and an interdisciplinary
journal, PNAS. Name instances in datasets 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
component, 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. Asian names were found
to account for the majority of misidentification by initial-based
disambiguation due to their common surname and given name initials.
