Before revision:  So, we redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity . TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods . Specifically, TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features) .
After revision:  So, we redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity , like traditional NA methods do. TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features) .