Before revision:  The PAFit-oneshot method corrects for a  bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot. This bias, which had previously gone unnoticed, has a connection with a recently developed conditional inference approach called post-selection inference. Extensive experiments show that our method recovers the true preferential attachment function in simulated as well as real-world networks. Our work opens up a new path for network scientists to measure preferential attachment in a large number of  one-snapshot networks that have been out-of-reach until now .
After revision:  The PAFit-oneshot method corrects for a bias that arises when estimating preferential attachment values only for degrees observed in the snapshot. Our work provides scientists with a means of measuring preferential attachment in a large number of one-snapshot networks that have been out-of-reach until now .