10 papers
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…
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
Li Sun, Ming Zhang, Wenxin Jin +5
Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Network…
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…
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
Li Sun, Zhenhao Huang, Ming Zhang +1
Message Passing Neural Networks (MPNNs) is the building block of graph foundation models, but fundamentally suffer from oversmoothing and oversquashing. There has recently been a s…
ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Space
Li Sun, Zhenhao Huang, Yujie Wang +4
Graph clustering is a longstanding topic in machine learning. Recently, deep methods have achieved results but still require predefined cluster numbers K and struggle with imbalanc…