In our method, we use a similar graph-based construction to train vector representations of a node (each node a word).
we use a stratified sampling approach within a maximum distance (hopcount) of 2, instead of allowing the random walk to proceed along in a combined depth-first and breadth-first manner, as in (Grover and Leskovec, 2016).