Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?
Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings
Large general purpose pre-trained models have revolutionized computer vision and natural
language understanding. However, the development of general purpose pre-trained Graph …
language understanding. However, the development of general purpose pre-trained Graph …
How Local isLocal'? Deep Learning Reveals Locality of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons
Y Davidson, A Philipp, S Chakraborty, AM Bronstein… - 2025 - chemrxiv.org
We investigate the locality of magnetic response in polycyclic aromatic molecules using a
novel deep-learning approach. Our method employs graph neural networks (GNNs) with a …
novel deep-learning approach. Our method employs graph neural networks (GNNs) with a …
Towards Neural Scaling Laws on Graphs
Deep graph models (eg, graph neural networks and graph transformers) have become
important techniques for leveraging knowledge across various types of graphs. Yet, the …
important techniques for leveraging knowledge across various types of graphs. Yet, the …
Fully-inductive Node Classification on Arbitrary Graphs
One fundamental challenge in graph machine learning is generalizing to new graphs. Many
existing methods following the inductive setup can generalize to test graphs with new …
existing methods following the inductive setup can generalize to test graphs with new …