论文标题
通过结构消息构建功能强大且模棱两可的图形神经网络
Building powerful and equivariant graph neural networks with structural message-passing
论文作者
论文摘要
事实证明，消息通讯是设计图形神经网络的有效方法，因为它能够利用置换量比和对学习局部结构的归纳偏见，以实现良好的概括。但是，当前的消息架构具有有限的表示能力，并且无法学习图形的基本拓扑特性。我们解决了这个问题，并基于两个想法提出了一个强大而模棱两可的消息串通框架：首先，除了功能之外，我们将节点的单热编码传播，以便学习每个节点周围的局部上下文矩阵。该矩阵包含有关功能和拓扑的丰富本地信息，最终可以汇总以构建节点表示。其次，我们提出了消息和更新功能的参数化的方法，以确保置换量比。具有独立于单热编码的特定选择的表示形式允许电感推理，并带来更好的概括属性。在实验上，我们的模型可以比以前的方法更准确地预测合成数据上的各种图形拓扑特性，并在锌数据集上的分子图回归中获得最新的结果。
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.
