Message-passing has proved to be an effective way to design graph neural
networks, as it is able to leverage both permutation equivariance and an
inductive bias towards learning local structures in order to achieve good
generalization. However, current message-passing architectures have a limited
representation power and fail to learn basic topological properties of graphs.
We address this problem and propose a powerful and equivariant message-passing
framework based on two ideas: first, we propagate a one-hot encoding of the
nodes, in addition to the features, in order to learn a local context matrix
around each node. This matrix contains rich local information about both
features and topology and can eventually be pooled to build node
representations. Second, we propose methods for the parametrization of the
message and update functions that ensure permutation equivariance. Having a
representation that is independent of the specific choice of the one-hot
encoding permits inductive reasoning and leads to better generalization
properties. Experimentally, our model can predict various graph topological
properties on synthetic data more accurately than previous methods and achieves
state-of-the-art results on molecular graph regression on the ZINC dataset.

通过在节点特征的基础上传播节点的 One-hot 编码，以学习每个节点周围的本地上下文矩阵以及保证置换等变性的消息和更新函数，我们提出了一种具有强大表达能力和等变性的消息传递框架，用于对图形网络进行建模，实验表明本模型可以更准确地预测合成数据上的图形拓扑性质，并在 ZINC 数据集的分子图回归任务中取得了最优结果。