Open Problem: Learning Dynamic Network Models from a Static Snapshot
Proceedings of the 25th Annual Conference on Learning Theory, PMLR 23:45.1-45.3, 2012.
In this paper we consider the problem of learning a graph generating process given the evolving graph at a single point in time. Given a graph of sufficient size, can we learn the (repeatable) process that generated it? We formalize the generic problem and then consider two simple instances which are variations on the well-know graph generation models by Erdós-Rényi and Albert-Barabasi.