  We consider the problem of perfectly recovering the vertex correspondence
between two correlated Erd\H{o}s-R\'enyi (ER) graphs. For a pair of correlated
graphs on the same vertex set, the correspondence between the vertices can be
obscured by randomly permuting the vertex labels of one of the graphs. In some
cases, the structural information in the graphs allow this correspondence to be
recovered. We investigate the information-theoretic threshold for exact
recovery, i.e. the conditions under which the entire vertex correspondence can
be correctly recovered given unbounded computational resources.
  Pedarsani and Grossglauser provided an achievability result of this type.
Their result establishes the scaling dependence of the threshold on the number
of vertices. We improve on their achievability bound. We also provide a
converse bound, establishing conditions under which exact recovery is
impossible. Together, these establish the scaling dependence of the threshold
on the level of correlation between the two graphs. The converse and
achievability bounds differ by a factor of two for sparse, significantly
correlated graphs.
