With the ubiquitous presence of networks in many areas of science and technology, a multitude of new challenges have gained importance in the statistical analysis of networks. One such area, termed network archaeology (Navlakha and Kingsford [26]) studies problems about unveiling the past of dynamically growing networks, based on present-day observations. In order to develop a sound statistical theory for such problems, one usually models the growing network by simple stochastic growth dynamics. Perhaps the most prominent such growth model is the preferential attachment model, advocated by Albert and Barabási [2]. In these models, vertices of the network arrive one by one and a new vertex attaches to one or more existing vertices by an edge according to some simple probabilistic rule. Arguably the simplest problem of network archaeology is that of root finding, when one aims at estimating the first vertex of a random network, based on observing the (unlabeled) network at a much later point of time.