3 citations · 6 across the 19 of their papers we have counts for
19 papers
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…
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
Kaiwen Luo, Zhenhong Zhou, Leyan Wang +32
Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…
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…
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…
Hyperbolic Continuous Structural Entropy for Hierarchical Clustering
Guangjie Zeng, Hao Peng, Angsheng Li +5
Hierarchical clustering is a fundamental machine-learning technique for grouping data points into dendrograms. However, existing hierarchical clustering methods encounter two prima…
Unsupervised Graph Clustering with Deep Structural Entropy
Jingyun Zhang, Hao Peng, Li Sun +3
Research on Graph Structure Learning (GSL) provides key insights for graph-based clustering, yet current methods like Graph Neural Networks (GNNs), Graph Attention Networks (GATs),…
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…
Motion Forecasting for Autonomous Vehicles: A Survey
Jianxin Shi, Jinhao Chen, Yuandong Wang +4
In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants…
Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing Dynamics
Li Sun, Ziheng Zhang, Zixi Wang +5
Dynamic interacting system modeling is important for understanding and simulating real world systems. The system is typically described as a graph, where multiple objects dynamical…
RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry
Li Sun, Zhenhao Huang, Suyang Zhou +3
The foundation model has heralded a new era in artificial intelligence, pretraining a single model to offer cross-domain transferability on different datasets. Graph neural network…
Structural Entropy Guided Probabilistic Coding
Xiang Huang, Hao Peng, Li Sun +4
Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity…
Spiking Graph Neural Network on Riemannian Manifolds
Li Sun, Zhenhao Huang, Qiqi Wan +2
Graph neural networks (GNNs) have become the dominant solution for learning on graphs, the typical non-Euclidean structures. Conventional GNNs, constructed with the Artificial Neur…
LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
Li Sun, Zhenhao Huang, Hao Peng +3
Graph clustering is a fundamental problem in machine learning. Deep learning methods achieve the state-of-the-art results in recent years, but they still cannot work without predef…
Relational Prompt-based Pre-trained Language Models for Social Event Detection
Pu Li, Xiaoyan Yu, Hao Peng +5
Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent…
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Li Sun, Zhenhao Huang, Hua Wu +4
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined gr…