Benchmarking Graph Neural Networks

Understand

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

• This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry.
• But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress.
• This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas.