  Integrating data from heterogeneous sources is often modeled as merging
graphs. Given two or more 'compatible', but not-isomorphic graphs, the first
step is to identify a graph alignment, where a potentially partial mapping of
vertices between two graphs is computed. A significant portion of the
literature on this problem only takes the global structure of the input graphs
into account. Only more recent ones additionally use vertex and edge attributes
to achieve a more accurate alignment. However, these methods are not designed
to scale to map large graphs arising in many modern applications. We propose a
new iterative graph aligner, gsaNA, that uses the global structure of the
graphs to significantly reduce the problem size and align large graphs with a
minimal loss of information. Concretely, we show that our proposed technique is
highly flexible, can be used to achieve higher recall, and it is orders of
magnitudes faster than the current state of the art techniques.
