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Network embedding aims to represent each node in a network as alow-dimensional feature vector that summarizes the given node's (extended)network neighborhood. The nodes' feature vectors can then be used in variousdownstream machine learning tasks. Recently, many embedding methods thatautomatically learn the features of nodes have emerged, such as node2vec andstruc2vec, which have been used in tasks such as node classification, linkprediction, and node clustering, mainly in the social network domain. There arealso other embedding methods that explicitly look at the connections betweennodes, i.e., the nodes' network neighborhoods, such as graphlets. Graphletshave been used in many tasks such as network comparison, link prediction, andnetwork clustering, mainly in the computational biology domain. Even though thetwo types of embedding methods (node2vec/struct2vec versus graphlets) have asimilar goal -- to represent nodes as features vectors, no comparisons havebeen made between them, possibly because they have originated in the differentdomains. Therefore, in this study, we compare graphlets to node2vec andstruc2vec, and we do so in the task of network alignment. In evaluations onsynthetic and real-world biological networks, we find that graphlets are bothmore accurate and faster than node2vec and struc2vec.

Graphlets versus node2vec and struc2vec in the task of network alignment