  In the field of node representation learning the task of interpreting latent
dimensions has become a prominent, well-studied research topic. The
contribution of this work focuses on appraising the interpretability of another
rarely-exploited feature of node embeddings increasingly utilised in
recommendation and consumption diversity studies: inter-node embedded
distances. Introducing a new method to measure how understandable the distances
between nodes are, our work assesses how well the proximity weights derived
from a network before embedding relate to the node closeness measurements after
embedding. Testing several classical node embedding models, our findings reach
a conclusion familiar to practitioners albeit rarely cited in literature - the
matrix factorisation model SVD is the most interpretable through 1, 2 and even
higher-order proximities.
