Before revision: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions characteristic of real-world networks depends on it. Multiple methods have been devised for measuring preferential attachment in time-resolved networks. However, many real-world network datasets areavailable as only single snapshots .
After revision: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions characteristic of 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. Numerous real-world network datasets areavailable as only single snapshots .