On the Scalability of GNNs for Molecular Graphs

arXiv:2404.11568v2 Announce Type: replace
Abstract: 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 …

abstract architectures arxiv benefits cs.lg dataset deep learning gnns graph graph neural networks graphs however image image generation language language modelling modelling networks neural networks performance relationship revolutions scalability scaling show type