In graph theory, the Weisfeiler Leman graph isomorphism test is a heuristic test for the existence of an isomorphism between two graphs G and H. [1] It is a generalization of the color refinement algorithm and has been first described by Weisfeiler and Leman in 1968. [2] The original formulation is based on graph canonization, a normal form for graphs, while there is also a combinatorial interpretation in the spirit of color refinement and a connection to logic.
An example of two non-isomorphic graphs that WLpair cannot distinguish is given here. [3]
The theory behind the Weisfeiler Leman test is applied in graph neural networks.
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. [4] These Weisfeiler Leman graph kernels have attracted considerable research in the decade after their publication. [1]