  Preferential attachment is commonly invoked to explain the emergence of those
heavy-tailed degree distributions characteristic of growing network
representations of diverse real-world phenomena. Experimentally confirming this
hypothesis in real-world growing networks is an important frontier in network
science research. Conventional preferential attachment estimation methods
require that a growing network be observed across at least two snapshots in
time. Numerous publicly available growing network datasets are, however, only
available as single snapshots, leaving the applied network scientist with no
means of measuring preferential attachment in these cases. We propose a
nonparametric method, called PAFit-oneshot, for estimating preferential
attachment in a growing network from one snapshot. PAFit-oneshot corrects for a
previously unnoticed bias that arises when estimating preferential attachment
values only for degrees observed in the single snapshot. Our work provides a
means of measuring preferential attachment in a large number of publicly
available one-snapshot networks. As a demonstration, we estimated preferential
attachment in three such networks, and found sublinear preferential attachment
in all cases. PAFit-oneshot is implemented in the R package PAFit.
