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.

本文介绍了一种二进制图卷积神经网络（Bi-GCN），它将网络参数和输入节点特征进行二元化，并将原始矩阵乘法改为二进制运算以加速计算，并使用一种新的梯度逼近反向传播方法来训练我们的 Bi-GCN。实验表明，与全精度基线相比，Bi-GCN 在节省内存资源方面平均可以减少约 30 倍，并平均加速推理速度约 47 倍，并且该二进制方法还可以轻松应用于其他 GNNs。