UvA-DARE (Digital Academic Repository) Natural Graph Networks
UvA-DARE (Digital Academic Repository) Natural Graph Networks
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发表时间：
2020
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通讯作者：
Pim de Haan Qualcomm;AI Research;Taco Cohen;M. Welling
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作者：
Pim de Haan Qualcomm;AI Research;Taco Cohen;M. Welling
图神经网络的一个关键要求是它们必须以不依赖于图的描述方式的方式处理图。传统上，这被认为意味着图网络必须与节点排列等变。在这里，我们证明，代替等变性，更一般的自然性概念足以定义良好的图网络，从而开辟更大的图网络类别。我们定义了全局和局部自然图网络，后者与传统消息传递图神经网络一样可扩展，同时更加灵活。我们在图上给出了自然网络的一个实际实例，它使用等变消息网络参数化，在多个基准测试中产生了良好的性能。
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.