Before revision: Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge in real-world networks, but also for gaining insight into various rich-get-richer effects that arise in diverse fields. Conventional estimation methods require that a  network be observed across at least two snapshots in time.
After revision: Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge in real-world growing networks is an important frontier in network science research. Conventional preferential attachment estimation methods require that a network be observed across at least two snapshots in time.