QUINT: Node Embedding Using Network Hashing

Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods (such as
deepwalk
,
node2vec
,
LINE
) are based on a neural architecture, thus unable to scale on large networks both in terms of time and space usage. Recently, we proposed
$\mathrm{BinSketch}$
, a sketching technique for compressing binary vectors to binary vectors. In this paper, we show how to extend
$\mathrm{BinSketch}$
and use it for network hashing. Our proposal named
QUINT
is built upon
$\mathrm{BinSketch}$
, and it embeds nodes of a sparse network onto a low-dimensional space using simple bit-wise operations.
QUINT
is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks. Extensive experiments are conducted to compare
QUINT
with seven state-of-the-art network embedding methods for two end tasks – link prediction and node classification. We observe huge performance gain for
QUINT
in terms of speedup (up to
$7000\times$
) and space saving (up to
$800\times$
) due to its bit-wise nature to obtain node embedding. Moreover,
QUINT
is a consistent top-performer for both the tasks among the baselines across all the datasets. Our empirical observations are backed by rigorous theoretical analysis to justify the effectiveness of
QUINT
. In particular, we prove that
QUINT
retains enough structural information which can be used further to approximate many topological properties of networks with high confidence.