  Recently, there have been some breakthroughs in graph analysis by applying
the graph neural networks (GNNs) following a neighborhood aggregation scheme,
which demonstrate outstanding performance in many tasks. However, we observe
that the parameters of the network and the embedding of nodes are represented
in real-valued matrices in existing GNN-based graph embedding approaches which
may limit the efficiency and scalability of these models. It is well-known that
binary vector is usually much more space and time efficient than the
real-valued vector. This motivates us to develop a binarized graph neural
network to learn the binary representations of the nodes with binary network
parameters following the GNN-based paradigm. Our proposed method can be
seamlessly integrated into the existing GNN-based embedding approaches to
binarize the model parameters and learn the compact embedding. Extensive
experiments indicate that the proposed binarized graph neural network, namely
BGN, is orders of magnitude more efficient in terms of both time and space
while matching the state-of-the-art performance.

Machine Learning;Machine Learning