Weisfeiler Leman graph isomorphism test

The theory behind the Weisfeiler Leman test may be applied in graph neural networks.^{[citation needed][how?]}

In machine learning of nonlinear data one uses kernels to represent the data in a high dimensional feature space after which linear techniques such as support vector machines can be applied. Data represented as graphs often behave nonlinearly. Graph kernels are a method to preprocess such graph based nonlinear data to simplify subsequent learning methods. Such graph kernels can be constructed by partially executing a Weisfeiler Leman test and processing the partition that has been constructed up to that point.^[5]