On the Scalability of GNNs for Molecular Graphs

Understand

Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation.

• Practitioners have observed a strong relationship between model size, dataset size, and performance.
• However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures.
• We address this drawback of GNNs by studying their scaling behavior.