Networks are often naturally modeled by random processes in which nodes of the network are added one-by-one, according to some random rule. Uniform and preferential attachment trees are among the simplest 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 snapshots observed. Such problems are sometimes termed "network archaeology". We present a few results that show that, even in gigantic networks, a lot of information is preserved from the very early days.
*Texto informado pelo autor.
Gábor Lugosi graduated in electrical engineering at the Technical University of Budapest in 1987, and received his Ph.D. from the Hungarian Academy of Sciences in 1991. Since 1996, he has been at the Department of Economics, Pompeu Fabra University, Barcelona. In 2006 he became an ICREA research professor. His research interests include the theory of machine learning, mathematical statistics, probability, random structures, and information theory.