  Recent advances in the field of network representation learning are mostly
attributed 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 of
neighbourhood and aim to find the vector representation for a node, which
maximizes the likelihood of preserving this neighborhood.
  In this paper, we take a drastic departure from the existing notion of
neighbourhood of a node by utilizing the idea of coreness. More specifically,
we utilize the well-established idea that nodes with similar core numbers play
equivalent roles in the network and hence induce a novel and an organic notion
of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic
framework for learning low dimensional continuous feature mapping for a node.
Consequently, the nodes having similar core numbers are relatively closer in
the vector space that we learn.
  We further demonstrate the effectiveness of core2vec by comparing word
similarity scores obtained by our method where the node representations are
drawn from standard word association graphs against scores computed by other
state-of-the-art network representation techniques like node2vec, DeepWalk and
LINE. Our results always outperform these existing methods
