  Network embedding techniques inspired by word2vec represent an effective
unsupervised relational learning model. Commonly, by means of a Skip-Gram
procedure, these techniques learn low dimensional vector representations of the
nodes in a graph by sampling node-context examples. Although many ways of
sampling the context of a node have been proposed, the effects of the way a
node is chosen have not been analyzed in depth. To fill this gap, we have
re-implemented the main four word2vec inspired graph embedding techniques under
the same framework and analyzed how different sampling distributions affects
embeddings performance when tested in node classification problems. We present
a set of experiments on different well known real data sets that show how the
use of popular centrality distributions in sampling leads to improvements,
obtaining speeds of up to 2 times in learning times and increasing accuracy in
all cases.
