A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
Graph self-supervised learning is now a go-to method for pre-training graph foundation
models, including graph neural networks, graph transformers, and more recent large …
models, including graph neural networks, graph transformers, and more recent large …
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
In this paper, we develop SE3Set, an SE (3) equivariant hypergraph neural network
architecture tailored for advanced molecular representation learning. Hypergraphs are not …
architecture tailored for advanced molecular representation learning. Hypergraphs are not …
Holistic Molecular Representation Learning via Multi-view Fragmentation
Learning chemically meaningful representations from unlabeled molecules plays a vital role
in AI-based drug design and discovery. In response to this, several self-supervised learning …
in AI-based drug design and discovery. In response to this, several self-supervised learning …