Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
Fuente:
arXiv
Saved in:
| Main Authors: | Yu, Xingtong, Zhou, Chang, Fang, Yuan, Zhang, Xinming |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
by: Yu, Xingtong, et al.
Published: (2025)
by: Yu, Xingtong, et al.
Published: (2025)
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
by: Yu, Xingtong, et al.
Published: (2023)
by: Yu, Xingtong, et al.
Published: (2023)
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
by: Yu, Xingtong, et al.
Published: (2023)
by: Yu, Xingtong, et al.
Published: (2023)
SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation
by: Yu, Xingtong, et al.
Published: (2025)
by: Yu, Xingtong, et al.
Published: (2025)
Learning to Count Isomorphisms with Graph Neural Networks
by: Yu, Xingtong, et al.
Published: (2023)
by: Yu, Xingtong, et al.
Published: (2023)
Node-Time Conditional Prompt Learning In Dynamic Graphs
by: Yu, Xingtong, et al.
Published: (2024)
by: Yu, Xingtong, et al.
Published: (2024)
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
by: Yu, Xingtong, et al.
Published: (2023)
by: Yu, Xingtong, et al.
Published: (2023)
Non-Homophilic Graph Pre-Training and Prompt Learning
by: Yu, Xingtong, et al.
Published: (2024)
by: Yu, Xingtong, et al.
Published: (2024)
Event-Aware Prompt Learning for Dynamic Graphs
by: Yu, Xingtong, et al.
Published: (2025)
by: Yu, Xingtong, et al.
Published: (2025)
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
by: Yu, Xingtong, et al.
Published: (2024)
by: Yu, Xingtong, et al.
Published: (2024)
LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training
by: Shan, Lianze, et al.
Published: (2026)
by: Shan, Lianze, et al.
Published: (2026)
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
by: Luo, Jiayi, et al.
Published: (2025)
by: Luo, Jiayi, et al.
Published: (2025)
Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
by: Yuan, Haonan, et al.
Published: (2026)
by: Yuan, Haonan, et al.
Published: (2026)
CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
by: Chen, Xuanze, et al.
Published: (2026)
by: Chen, Xuanze, et al.
Published: (2026)
Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights
by: Yu, Xingtong, et al.
Published: (2026)
by: Yu, Xingtong, et al.
Published: (2026)
GCoT: Chain-of-Thought Prompt Learning for Graphs
by: Yu, Xingtong, et al.
Published: (2025)
by: Yu, Xingtong, et al.
Published: (2025)
Towards Faster Graph Partitioning via Pre-training and Inductive Inference
by: Qin, Meng, et al.
Published: (2024)
by: Qin, Meng, et al.
Published: (2024)
Towards Pre-trained Graph Condensation via Optimal Transport
by: Yan, Yeyu, et al.
Published: (2025)
by: Yan, Yeyu, et al.
Published: (2025)
Task-Aware Adaptive Modulation: A Replay-Free and Resource-Efficient Approach For Continual Graph Learning
by: Liu, Jingtao, et al.
Published: (2025)
by: Liu, Jingtao, 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)
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
by: Zhu, Yun, et al.
Published: (2023)
by: Zhu, Yun, et al.
Published: (2023)
Graph Foundation Models: Concepts, Opportunities and Challenges
by: Liu, Jiawei, et al.
Published: (2023)
by: Liu, Jiawei, et al.
Published: (2023)
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)
Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
by: Mattos, João, et al.
Published: (2026)
by: Mattos, João, et al.
Published: (2026)
Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks
by: Tang, Ziyuan, et al.
Published: (2025)
by: Tang, Ziyuan, 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)
Endowing Pre-trained Graph Models with Provable Fairness
by: Zhang, Zhongjian, et al.
Published: (2024)
by: Zhang, Zhongjian, et al.
Published: (2024)
Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
by: Liang, Chundong, et al.
Published: (2026)
by: Liang, Chundong, 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)
TAAM:Inductive Graph-Class Incremental Learning with Task-Aware Adaptive Modulation
by: Liu, Jingtao, et al.
Published: (2026)
by: Liu, Jingtao, et al.
Published: (2026)
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)
FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
by: Wang, Zhili, et al.
Published: (2023)
by: Wang, Zhili, et al.
Published: (2023)
Graph Generative Pre-trained Transformer
by: Chen, Xiaohui, et al.
Published: (2025)
by: Chen, Xiaohui, et al.
Published: (2025)
GraphGPT: Generative Pre-trained Graph Eulerian Transformer
by: Zhao, Qifang, et al.
Published: (2023)
by: Zhao, Qifang, et al.
Published: (2023)
Towards Graph Foundation Models for Personalization
by: Damianou, Andreas, et al.
Published: (2024)
by: Damianou, Andreas, et al.
Published: (2024)
Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphs
by: Sun, Shengyin, et al.
Published: (2025)
by: Sun, Shengyin, et al.
Published: (2025)
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
by: Chen, Zhikai, et al.
Published: (2024)
by: Chen, Zhikai, et al.
Published: (2024)
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)
Diffusion-Guided Pretraining for Brain Graph Foundation Models
by: Wei, Xinxu, et al.
Published: (2026)
by: Wei, Xinxu, et al.
Published: (2026)
Similar Items
-
MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
by: Yu, Xingtong, et al.
Published: (2025) -
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
by: Yu, Xingtong, et al.
Published: (2023) -
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
by: Yu, Xingtong, et al.
Published: (2023) -
SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation
by: Yu, Xingtong, et al.
Published: (2025) -
Learning to Count Isomorphisms with Graph Neural Networks
by: Yu, Xingtong, et al.
Published: (2023)