  Several studies have found that collaboration networks are scale-free,
proposing that such networks can be modeled by specific network evolution
mechanisms like preferential attachment. This study argues that collaboration
networks can look more or less scale-free depending on the methods for
resolving author name ambiguity in bibliographic data. Analyzing networks
constructed from multiple datasets containing 3.4M ~ 9.6M publication records,
this study shows that collaboration networks in which author names are
disambiguated by the commonly used heuristic, i.e., forename-initial-based name
matching, tend to produce degree distributions better fitted to power-law
slopes with the typical scaling parameter (2 < {\alpha} < 3) than networks
disambiguated by more accurate algorithm-based methods. Such tendency is
observed across collaboration networks generated under various conditions such
as cumulative years, 5- & 1-year sliding windows, and random sampling, and
through simulation, found to arise due mainly to artefactual entities created
by inaccurate disambiguation. This cautionary study calls for special attention
from scholars analyzing network data in which entities such as people,
organization, and gene can be merged or split by improper disambiguation.
