Gradual Weisfeiler-Leman: Slow and Steady Wins the Race

TL;DR

This paper introduces a gradual refinement framework for the Weisfeiler-Leman algorithm, enabling more nuanced graph similarity measures that improve kernel and graph edit distance tasks with moderate computational overhead.

Contribution

It generalizes color refinement to allow slower convergence, creating a more detailed hierarchy of vertex similarities for enhanced graph analysis.

Findings

Outperforms original color refinement in graph kernels

Improves graph edit distance approximation

Maintains moderate computational cost

Abstract

The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for graph learning with kernels and neural networks. Originally developed for graph isomorphism testing, the algorithm iteratively refines vertex colors. On many datasets, the stable coloring is reached after a few iterations and the optimal number of iterations for machine learning tasks is typically even lower. This suggests that the colors diverge too fast, defining a similarity that is too coarse. We generalize the concept of color refinement and propose a framework for gradual neighborhood refinement, which allows a slower convergence to the stable coloring and thus provides a more fine-grained refinement hierarchy and vertex similarity. We assign new colors by clustering vertex neighborhoods, replacing the original injective color assignment function. Our approach is used to derive new variants of existing graph kernels and to approximate the graph edit distance via optimal assignments regarding vertex similarity. We show that in both tasks, our method outperforms the original color refinement with only a moderate increase in running time advancing the state of the art.