论文标题
Tudataset：用于使用图形学习的基准数据集的集合
TUDataset: A collection of benchmark datasets for learning with graphs
论文作者
论文摘要
最近，使用图形数据，尤其是使用图神经网络，人们对（监督）学习的兴趣越来越大。但是，有意义的基准数据集和标准化评估程序的开发正在滞后，因此阻碍了该领域的进步。为了解决这个问题，我们介绍了tudataset用于图形分类和回归。该集合包含来自各种应用程序的120多个不同大小的数据集。我们提供基于Python的数据加载程序，内核和图形神经网络基线实现以及评估工具。在这里，我们概述了数据集，标准化评估程序并提供基线实验。所有数据集可在www.graphlearning.io上找到。从www.github.com/chrsmrrs/tudataset上获得的代码可完全重现实验。
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.
