Networks are often naturally modeled by random processes in which nodes and edges of the network are added one-by-one, according to some simple stochastic dynamics. Uniform and preferential attachment processes are prime examples of such dynamically growing networks. The statistical problems we address in this talk regard discovering the past of the network when a present-day snapshot is observed. Such problems are sometimes termed 'network archeology'. We present a few results that show that, even in gigantic networks, a lot of information is preserved from the very early days. As the field is still in its infancy, many interesting questions remain to be explored.[-]

Networks are often naturally modeled by random processes in which nodes and edges of the network are added one-by-one, according to some simple stochastic dynamics. Uniform and preferential attachment processes are prime examples of such dynamically growing networks. The statistical problems we address in this talk regard discovering the past of the network when a present-day snapshot is observed. Such problems are sometimes termed 'network archeology'. We present a few results that show that, even in gigantic networks, a lot of information is preserved from the very early days. As the field is still in its infancy, many interesting questions remain to be explored.[-]