  This paper discusses how to generate general graph node embeddings from
knowledge graph representations. The embedded space is composed of a number of
sub-features to mimic both local affinity and remote structural relevance.
These sub-feature dimensions are defined by several indicators that we
speculate to catch nodal similarities, such as hop-based topological patterns,
the number of overlapping labels, the transitional probabilities (markov-chain
probabilities), and the cluster indices computed by our recursive spectral
bisection (RSB) algorithm. These measures are flattened over the one
dimensional vector space into their respective sub-component ranges such that
the entire set of vector similarity functions could be used for finding similar
nodes. The error is defined by the sum of pairwise square differences across a
randomly selected sample of graph nodes between the assumed embeddings and the
ground truth estimates as our novel loss function. The ground truth is
estimated to be a combination of pairwise Jaccard similarity and the number of
overlapping labels. Finally, we demonstrate a multi-variate stochastic gradient
descent (SGD) algorithm to compute the weighing factors among sub-vector spaces
to minimize the average error using a random sampling logic.
