Yifan Feng

Hypergraph Neural Networks: Theoretical Foundations and Applications.

Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling

Hyper-Fold is introduced, a rank-K separable convolutional backbone approaching this ceiling at message-passing cost, suggesting that a sufficiently expressive 3D backbone recovers information that fusion architectures previously borrowed from evolution-scale pretraining.

Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling

Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (three-dimensional geometry). What is the expressive limit of this layer class? We show that the complete bilinear operator over content-geometry outer products--the suffi...

How Powerful are Hypergraph Neural Networks?

The Hypergraph Identity-Aware Subtree (IA Subtree) Kernel is introduced, which distinguishes uniform-regular hypergraphs by considering both neighborhood connectivity and connection density and develops two Hypergraph Neural Networks: Hypergraph Isomorphism Networks (HGIN) and Identity-Aware Hypergraph Isomorphism Netw...