经典的Weisfeiler-Leman算法（又称颜色的细化是图形学习的基础，对于成功的图形内核和图形神经网络至关重要。该算法最初是用于图形同构测试的，它迭代地完善了顶点颜色。在许多数据集中，经过一些迭代后，可以达到稳定的着色，并且机器学习任务的最佳迭代数量通常更低。这表明颜色差异太快，定义了一个太粗糙的相似性。我们概括了颜色改进的概念，并提出了一个逐步邻里改进的框架，该框架使收敛较慢，从而提供了更细粒度的完善层次结构和顶点相似性。我们通过聚类顶点邻域来分配新颜色，从而替换原始的注射颜色分配功能。我们的方法用于得出现有图形内核的新变体，并通过有关顶点相似性的最佳分配来近似图表编辑距离。我们表明，在这两个任务中，我们的方法的表现都优于原始颜色的细化，只有在运行时间中逐渐增加，才能提高最新技术状态。

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDATASET for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.