  Word and graph embeddings are widely used in deep learning applications. We
present a data structure that captures inherent hierarchical properties from an
unordered flat embedding space, particularly a sense of direction between pairs
of entities. Inspired by the notion of \textit{distributional generality}, our
algorithm constructs an arborescence (a directed rooted tree) by inserting
nodes in descending order of entity power (e.g., word frequency), pointing each
entity to the closest more powerful node as its parent.
  We evaluate the performance of the resulting tree structures on three tasks:
hypernym relation discovery, least-common-ancestor (LCA) discovery among words,
and Wikipedia page link recovery. We achieve average 8.98\% and 2.70\% for
hypernym and LCA discovery across five languages and 62.76\% accuracy on
directed Wiki-page link recovery, with both substantially above baselines.
Finally, we investigate the effect of insertion order, the power/similarity
trade-off and various power sources to optimize parent selection.
