  While most network embedding techniques model the relative positions of nodes
in a network, recently there has been significant interest in structural
embeddings that model node role equivalences, irrespective of their distances
to any specific nodes. We present PhUSION, a proximity-based unified framework
for computing structural and positional node embeddings, which leverages
well-established methods for calculating node proximity scores. Clarifying a
point of contention in the literature, we show which step of PhUSION produces
the different kinds of embeddings and what steps can be used by both. Moreover,
by aggregating the PhUSION node embeddings, we obtain graph-level features that
model information lost by previous graph feature learning and kernel methods.
In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35
methods, we systematically reveal successful design choices for node and
graph-level machine learning with embeddings.
