  Learning accurate low-dimensional embeddings for a network is a crucial task
as it facilitates many downstream network analytics tasks. For large networks,
the trained embeddings often require a significant amount of space to store,
making storage and processing a challenge. Building on our previous work on
semi-supervised network embedding, we develop d-SNEQ, a differentiable
DNN-based quantisation method for network embedding. d-SNEQ incorporates a rank
loss to equip the learned quantisation codes with rich high-order information
and is able to substantially compress the size of trained embeddings, thus
reducing storage footprint and accelerating retrieval speed. We also propose a
new evaluation metric, path prediction, to fairly and more directly evaluate
model performance on the preservation of high-order information. Our evaluation
on four real-world networks of diverse characteristics shows that d-SNEQ
outperforms a number of state-of-the-art embedding methods in link prediction,
path prediction, node classification, and node recommendation while being far
more space- and time-efficient.
