  Data on molecular interactions is increasing at a tremendous pace, while the
development of solid methods for analyzing this network data is lagging behind.
This holds in particular for the field of comparative network analysis, where
one wants to identify commonalities between biological networks. Since
biological functionality primarily operates at the network level, there is a
clear need for topology-aware comparison methods.
  In this paper we present a method for global network alignment that is fast
and robust, and can flexibly deal with various scoring schemes taking both
node-to-node correspondences as well as network topologies into account. It is
based on an integer linear programming formulation, generalizing the
well-studied quadratic assignment problem. We obtain strong upper and lower
bounds for the problem by improving a Lagrangian relaxation approach and
introduce the software tool natalie 2.0, a publicly available implementation of
our method. In an extensive computational study on protein interaction networks
for six different species, we find that our new method outperforms alternative
state-of-the-art methods with respect to quality and running time.
