13 papers · 1 filter
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Li Sun, Zhenhao Huang, Yiding Wang +3
Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain ric…
ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
Chengze Li, Lingwei Wei, Li Sun +7
Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a single reusable solver. On…
Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence
Philip S. Yu, Li Sun
Graphs provide a natural description of the complex relationships among objects, and play a pivotal role in communications, transportation, social computing, the life sciences, etc…
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
Li Sun, Zhenhao Huang, Silei Chen +4
Multi-domain graph pre-training integrates knowledge from diverse domains to enhance performance in the target domains, which is crucial for building graph foundation models. Despi…
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
Li Sun, Lanxu Yang, Jiayu Tian +6
Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typica…
RiemannGL: Riemannian Geometry Changes Graph Deep Learning
Li Sun, Qiqi Wan, Suyang Zhou +2
Graphs are ubiquitous, and learning on graphs has become a cornerstone in artificial intelligence and data mining communities. Unlike pixel grids in images or sequential structures…