  Graphs or networks are a very convenient way to represent data with lots of
interaction. Recently, Machine Learning on Graph data has gained a lot of
traction. In particular, vertex classification and missing edge detection have
very interesting applications, ranging from drug discovery to recommender
systems. To achieve such tasks, tremendous work has been accomplished to learn
embedding of nodes and edges into finite-dimension vector spaces. This task is
called Graph Representation Learning. However, Graph Representation Learning
techniques often display prohibitive time and memory complexities, preventing
their use in real-time with business size graphs. In this paper, we address
this issue by leveraging a degeneracy property of Graphs - the K-Core
Decomposition. We present two techniques taking advantage of this decomposition
to reduce the time and memory consumption of walk-based Graph Representation
Learning algorithms. We evaluate the performances, expressed in terms of
quality of embedding and computational resources, of the proposed techniques on
several academic datasets. Our code is available at
https://github.com/SBrandeis/kcore-embedding
