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Recent advances in the field of network representation learning are mostlyattributed to the application of the skip-gram model in the context of graphs.State-of-the-art analogues of skip-gram model in graphs define a notion ofneighbourhood and aim to find the vector representation for a node, whichmaximizes the likelihood of preserving this neighborhood.  In this paper, we take a drastic departure from the existing notion ofneighbourhood of a node by utilizing the idea of coreness. More specifically,we utilize the well-established idea that nodes with similar core numbers playequivalent roles in the network and hence induce a novel and an organic notionof neighbourhood. Based on this idea, we propose core2vec, a new algorithmicframework for learning low dimensional continuous feature mapping for a node.Consequently, the nodes having similar core numbers are relatively closer inthe vector space that we learn.  We further demonstrate the effectiveness of core2vec by comparing wordsimilarity scores obtained by our method where the node representations aredrawn from standard word association graphs against scores computed by otherstate-of-the-art network representation techniques like node2vec, DeepWalk andLINE. Our results always outperform these existing methods

Core2Vec: A core-preserving feature learning framework for networks