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.

最近，通过遵循邻域聚合方案应用图神经网络（GNN），在图分析中取得了一些突破，这在许多任务中都表现出出色的性能。然而，我们观察到在现有的基于GNN的图嵌入方法中，网络的参数和节点的嵌入以实值矩阵表示，这可能会限制这些模型的效率和可伸缩性。众所周知，二元向量通常比实值向量具有更高的空间和时间效率。这激励我们开发二值化图神经网络，以遵循基于GNN的范例学习具有二进制网络参数的节点的二进制表示形式。我们提出的方法可以无缝地集成到现有的基于GNN的嵌入方法中，以对模型参数进行二值化并学习紧凑的嵌入。大量的实验表明，所提出的二值化图神经网络，即BGN，在时间和空间方面都更有效，而且与最新的性能相匹配。