  Graph Neural Networks (GNNs) have achieved tremendous success in graph
representation learning. Unfortunately, current GNNs usually rely on loading
the entire attributed graph into network for processing. This implicit
assumption may not be satisfied with limited memory resources, especially when
the attributed graph is large. In this paper, we pioneer to propose a Binary
Graph Convolutional Network (Bi-GCN), which binarizes both the network
parameters and input node features. Besides, the original matrix
multiplications are revised to binary operations for accelerations. According
to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an
average of ~30x for both the network parameters and input data, and accelerate
the inference speed by an average of ~47x, on the citation networks. Meanwhile,
we also design a new gradient approximation based back-propagation method to
train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN
can give a comparable performance compared to the full-precision baselines.
Besides, our binarization approach can be easily applied to other GNNs, which
has been verified in the experiments.
