Networks are frequently used to represent and study pairwise interactions between a collection of objects or individuals under study, and have become objects of study in their own right. In this talk I will describe a procedure called NetOTC (network optimal transition coupling) that can be used to compare and align two networks. The networks of interest may be directed or undirected, weighted or unweighted, and may have distinct vertex sets of different sizes. Given two networks and a cost function relating their vertices, NetOTC finds a transition coupling of their associated random walks having minimum expected cost. The minimizing cost quantifies the difference between the networks, while the optimal transport plan itself provides alignments of the vertices and edges of the two networks. Coupling of the full random walks ensures that NetOTC captures local and global information about the networks and preserves edges. I will review some useful theoretical properties of NetOTC, and present numerical experiments supporting its performance.