论文标题
自然图网络
Natural Graph Networks
论文作者
论文摘要
图形神经网络的关键要求是，它们必须以不取决于描述图的方式处理图形。传统上，这意味着图形网络必须等效于节点排列。在这里，我们表明，自然的更一般的自然概念不仅仅足以使图网络定义明确，从而打开了较大类别的图形网络。我们定义了全局和本地自然图网络，后者与传统消息传递图形神经网络一样可扩展，同时更加灵活。我们在图形上对自然网络进行了一个实际的实例化，该图形使用了模棱两可的消息网络参数化，从而在多个基准上产生了良好的性能。
A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean that a graph network must be equivariant to node permutations. Here we show that instead of equivariance, the more general concept of naturality is sufficient for a graph network to be well-defined, opening up a larger class of graph networks. We define global and local natural graph networks, the latter of which are as scalable as conventional message passing graph neural networks while being more flexible. We give one practical instantiation of a natural network on graphs which uses an equivariant message network parameterization, yielding good performance on several benchmarks.
