"graph foundation models" Papers
14 papers found
Conference
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
Li Sun, Zhenhao Huang, Ming Zhang et al.
NEURIPS 2025arXiv:2510.17457
1
citations
Enhanced Expert Merging for Mixture-of-Experts in Graph Foundation Models
Lei Liu, Xingyu Xia, Qianqian Xie et al.
NEURIPS 2025
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, Ismail Ilkan Ceylan, Michael Bronstein et al.
NEURIPS 2025arXiv:2506.14291
12
citations
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
Dexiong Chen, Markus Krimmel, Karsten Borgwardt
NEURIPS 2025arXiv:2502.02216
4
citations
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
Lecheng Kong, Jiarui Feng, Hao Liu et al.
ICLR 2025arXiv:2407.09709
30
citations
GraphGPT: Generative Pre-trained Graph Eulerian Transformer
Qifang Zhao, Weidong Ren, Tianyu Li et al.
ICML 2025arXiv:2401.00529
11
citations
GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
Gleb Bazhenov, Oleg Platonov, Liudmila Prokhorenkova
NEURIPS 2025oralarXiv:2409.14500
8
citations
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
Enjun Du, Xunkai Li, Tian Jin et al.
NEURIPS 2025spotlightarXiv:2504.00711
17
citations
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
Zihao Guo, Qingyun Sun, Haonan Yuan et al.
AAAI 2025paperarXiv:2412.11085
17
citations
GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
Haonan Yuan, Qingyun Sun, Junhua Shi et al.
NEURIPS 2025arXiv:2511.05592
4
citations
How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees
Haonan Yuan, Qingyun Sun, Junhua Shi et al.
ICML 2025
10
citations
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
Shuo Wang, Bokui Wang, Zhixiang Shen et al.
ICML 2025arXiv:2502.02017
19
citations
The Underappreciated Power of Vision Models for Graph Structural Understanding
Xinjian Zhao, Wei Pang, Zhongkai Xue et al.
NEURIPS 2025arXiv:2510.24788
2
citations
Position: Graph Foundation Models Are Already Here
Haitao Mao, Zhikai Chen, Wenzhuo Tang et al.
ICML 2024spotlightarXiv:2402.02216
79
citations