Network alignment aims to find regions of topological or functional similarities between networks. In computational biology, it can be used to transfer biological knowledge from a well-studied species to a poorly-studied species between aligned network regions. Typically, existing network aligners first compute similarities between nodes in different networks (via a node cost function) and then aim to find a high-scoring alignment (node mapping between the networks) with respect to “node conservation”, typically the total node cost function over all aligned nodes. Only after an alignment is constructed, the existing methods evaluate its quality with respect to an alternative measure, such as “edge conservation”. Thus, we recently aimed to directly optimize edge conservation while constructing an alignment, which improved alignment quality. Here, we approach a different idea of maximizing both node and edge conservation, and we also approach this idea from a novel perspective, by aligning optimally edges between networks first in order to improve node cost function needed to then align well nodes between the networks. In the process, unlike the existing measures of edge conservation that treat each conserved edge the same, we favor conserved edges that are topologically similar over conserved edges that are topologically dissimilar. We show that our proposed method, which we call GRaphlet Edge AlignmenT (GREAT), improves upon state-of-the-art methods that aim to optimize node conservation only or edge conservation only. Visit http:/d.edu/~cone/GREAT to access GREAT's implementation.
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