Learning Graph Foundation Models on Riemannian Graph-of-Graphs
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Haokun, Ding, Zezhong, Xie, Xike |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
by: Wang, Shangyou, et al.
Published: (2025)
by: Wang, Shangyou, et al.
Published: (2025)
DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion
by: Li, Jin, et al.
Published: (2025)
by: Li, Jin, et al.
Published: (2025)
Gaussian Relational Graph Transformer
by: Ding, Zezhong, et al.
Published: (2026)
by: Ding, Zezhong, et al.
Published: (2026)
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
by: Sun, Li, et al.
Published: (2026)
by: Sun, Li, et al.
Published: (2026)
RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry
by: Sun, Li, et al.
Published: (2025)
by: Sun, Li, et al.
Published: (2025)
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
by: Sun, Li, et al.
Published: (2025)
by: Sun, Li, et al.
Published: (2025)
GraphProp: Training the Graph Foundation Models using Graph Properties
by: Sun, Ziheng, et al.
Published: (2025)
by: Sun, Ziheng, et al.
Published: (2025)
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding
by: Cao, Yukun, et al.
Published: (2024)
by: Cao, Yukun, et al.
Published: (2024)
Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence
by: Yu, Philip S., et al.
Published: (2026)
by: Yu, Philip S., et al.
Published: (2026)
See or Say Graphs: Agent-Driven Scalable Graph Structure Understanding with Vision-Language Models
by: Han, Shuo, et al.
Published: (2025)
by: Han, Shuo, et al.
Published: (2025)
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
by: Xu, Yuhao, et al.
Published: (2024)
by: Xu, Yuhao, et al.
Published: (2024)
Spectro-Riemannian Graph Neural Networks
by: Grover, Karish, et al.
Published: (2025)
by: Grover, Karish, et al.
Published: (2025)
GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
by: Zhu, Yun, et al.
Published: (2024)
by: Zhu, Yun, et al.
Published: (2024)
AnyGraph: Graph Foundation Model in the Wild
by: Xia, Lianghao, et al.
Published: (2024)
by: Xia, Lianghao, et al.
Published: (2024)
Relation-Aware Graph Foundation Model
by: Yu, Jianxiang, et al.
Published: (2025)
by: Yu, Jianxiang, et al.
Published: (2025)
Foundations and Frontiers of Graph Learning Theory
by: Huang, Yu, et al.
Published: (2024)
by: Huang, Yu, et al.
Published: (2024)
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
by: Zhu, Xi, et al.
Published: (2025)
by: Zhu, Xi, et al.
Published: (2025)
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
by: He, Yufei, et al.
Published: (2024)
by: He, Yufei, et al.
Published: (2024)
Turning Tabular Foundation Models into Graph Foundation Models
by: Eremeev, Dmitry, et al.
Published: (2025)
by: Eremeev, Dmitry, et al.
Published: (2025)
GraphPFN: A Prior-Data Fitted Graph Foundation Model
by: Eremeev, Dmitry, et al.
Published: (2025)
by: Eremeev, Dmitry, et al.
Published: (2025)
Adaptive Riemannian Graph Neural Networks
by: Wang, Xudong, et al.
Published: (2025)
by: Wang, Xudong, et al.
Published: (2025)
Riemannian Liquid Spatio-Temporal Graph Network
by: Lu, Liangsi, et al.
Published: (2026)
by: Lu, Liangsi, et al.
Published: (2026)
Tabular Foundation Models are Strong Graph Anomaly Detectors
by: Liu, Yunhui, et al.
Published: (2026)
by: Liu, Yunhui, et al.
Published: (2026)
Spiking Graph Neural Network on Riemannian Manifolds
by: Sun, Li, et al.
Published: (2024)
by: Sun, Li, et al.
Published: (2024)
Graph Foundation Models: Concepts, Opportunities and Challenges
by: Liu, Jiawei, et al.
Published: (2023)
by: Liu, Jiawei, et al.
Published: (2023)
Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
by: Wang, Kai, et al.
Published: (2024)
by: Wang, Kai, et al.
Published: (2024)
Graph Foundation Models: Bridging Language Model Paradigms and Graph Optimization
by: Liang, Yunhao, et al.
Published: (2025)
by: Liang, Yunhao, et al.
Published: (2025)
GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
by: Yuan, Haonan, et al.
Published: (2025)
by: Yuan, Haonan, et al.
Published: (2025)
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
by: Xu, Haoyan, et al.
Published: (2025)
by: Xu, Haoyan, et al.
Published: (2025)
Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning
by: Ding, Zezhong, et al.
Published: (2024)
by: Ding, Zezhong, et al.
Published: (2024)
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
by: Sun, Li, et al.
Published: (2024)
by: Sun, Li, et al.
Published: (2024)
RiemannGL: Riemannian Geometry Changes Graph Deep Learning
by: Sun, Li, et al.
Published: (2026)
by: Sun, Li, et al.
Published: (2026)
Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach
by: Zhu, Yinlin, et al.
Published: (2026)
by: Zhu, Yinlin, et al.
Published: (2026)
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
by: Yu, Xingtong, et al.
Published: (2024)
by: Yu, Xingtong, et al.
Published: (2024)
Out-of-Distribution Generalization in Graph Foundation Models
by: Li, Haoyang, et al.
Published: (2026)
by: Li, Haoyang, et al.
Published: (2026)
Can Graphs Improve Tabular Foundation Models?
by: Le, Franck, et al.
Published: (2025)
by: Le, Franck, et al.
Published: (2025)
Position: Graph Foundation Models are Already Here
by: Mao, Haitao, et al.
Published: (2024)
by: Mao, Haitao, et al.
Published: (2024)
OpenGraph: Towards Open Graph Foundation Models
by: Xia, Lianghao, et al.
Published: (2024)
by: Xia, Lianghao, et al.
Published: (2024)
Molecular Graph Contrastive Learning with Line Graph
by: Chen, Xueyuan, et al.
Published: (2025)
by: Chen, Xueyuan, et al.
Published: (2025)
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
by: Pan, Junjun, et al.
Published: (2025)
by: Pan, Junjun, et al.
Published: (2025)
Similar Items
-
SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
by: Wang, Shangyou, et al.
Published: (2025) -
DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion
by: Li, Jin, et al.
Published: (2025) -
Gaussian Relational Graph Transformer
by: Ding, Zezhong, et al.
Published: (2026) -
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
by: Sun, Li, et al.
Published: (2026) -
RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry
by: Sun, Li, et al.
Published: (2025)