TUDataset: A collection of benchmark datasets for learning with graphs

TUDataset: A collection of benchmark datasets for learning with graphs

Abstract

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.

Grafik Top
Authors
  • Morris, Christopher
  • Kriege, Nils M.
  • Bause, Franka
  • Kersting, Kristian
  • Mutzel, Petra
  • Neumann, Marion
Grafik Top
Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)
Divisions
Data Mining and Machine Learning
Subjects
Kuenstliche Intelligenz
Event Location
Vienna, Austria
Event Type
Workshop
Event Dates
17 July 2020
Date
2020
Official URL
www.graphlearning.io
Export
Grafik Top