Graph Foundation Models: Concepts, Opportunities and Challenges
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Jiawei, Yang, Cheng, Lu, Zhiyuan, Chen, Junze, Li, Yibo, Zhang, Mengmei, Bai, Ting, Fang, Yuan, Sun, Lichao, Yu, Philip S., Shi, Chuan |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
by: Li, Yibo, et al.
Published: (2024)
by: Li, Yibo, 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)
Data-centric Federated Graph Learning with Large Language Models
by: Yan, Bo, et al.
Published: (2025)
by: Yan, Bo, et al.
Published: (2025)
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
by: Zhang, Zhongjian, et al.
Published: (2024)
by: Zhang, Zhongjian, et al.
Published: (2024)
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
by: Zhang, Zhongjian, et al.
Published: (2026)
by: Zhang, Zhongjian, et al.
Published: (2026)
Provable Training for Graph Contrastive Learning
by: Yu, Yue, et al.
Published: (2023)
by: Yu, Yue, et al.
Published: (2023)
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
by: Zhang, Zhongjian, et al.
Published: (2026)
by: Zhang, Zhongjian, et al.
Published: (2026)
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
by: Zhang, Mengmei, et al.
Published: (2024)
by: Zhang, Mengmei, et al.
Published: (2024)
GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations
by: Chen, Junze, et al.
Published: (2025)
by: Chen, Junze, et al.
Published: (2025)
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
by: Chen, Junze, et al.
Published: (2025)
by: Chen, Junze, et al.
Published: (2025)
Minimum Topology Attacks for Graph Neural Networks
by: Zhang, Mengmei, et al.
Published: (2024)
by: Zhang, Mengmei, et al.
Published: (2024)
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
by: Chen, Boyu, et al.
Published: (2025)
by: Chen, Boyu, et al.
Published: (2025)
Graph Foundation Models for Recommendation: A Comprehensive Survey
by: Wu, Bin, et al.
Published: (2025)
by: Wu, Bin, et al.
Published: (2025)
Invariant debiasing learning for recommendation via biased imputation
by: Bai, Ting, et al.
Published: (2024)
by: Bai, Ting, 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)
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)
Graph World Models: Concepts, Taxonomy, and Future Directions
by: Liu, Jiawei, et al.
Published: (2026)
by: Liu, Jiawei, et al.
Published: (2026)
Foundation Models for CPS-IoT: Opportunities and Challenges
by: Baris, Ozan, et al.
Published: (2025)
by: Baris, Ozan, et al.
Published: (2025)
Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
by: Zhao, Zihao, et al.
Published: (2025)
by: Zhao, Zihao, et al.
Published: (2025)
Exploring the Potential of Large Language Models for Heterophilic Graphs
by: Wu, Yuxia, et al.
Published: (2024)
by: Wu, Yuxia, et al.
Published: (2024)
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare
by: Li, Xingyu, et al.
Published: (2024)
by: Li, Xingyu, et al.
Published: (2024)
CLMN: Concept based Language Models via Neural Symbolic Reasoning
by: Yang, Yibo
Published: (2025)
by: Yang, Yibo
Published: (2025)
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
by: Sun, Li, et al.
Published: (2026)
by: Sun, Li, et al.
Published: (2026)
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)
GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
by: Bai, Ting, et al.
Published: (2024)
by: Bai, Ting, et al.
Published: (2024)
Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models
by: Zhou, Rong, et al.
Published: (2026)
by: Zhou, Rong, et al.
Published: (2026)
RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
by: Yang, Jinyu, et al.
Published: (2026)
by: Yang, Jinyu, et al.
Published: (2026)
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
by: He, Yuting, et al.
Published: (2024)
by: He, Yuting, et al.
Published: (2024)
A Generalized Neural Diffusion Framework on Graphs
by: Li, Yibo, et al.
Published: (2023)
by: Li, Yibo, et al.
Published: (2023)
Foundation Models in Augmentative and Alternative Communication: Opportunities and Challenges
by: Di Paola, Ambra, et al.
Published: (2024)
by: Di Paola, Ambra, et al.
Published: (2024)
Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities
by: Goodge, Adam, et al.
Published: (2025)
by: Goodge, Adam, et al.
Published: (2025)
Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models
by: Liu, Yuqing, et al.
Published: (2024)
by: Liu, Yuqing, et al.
Published: (2024)
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)
Bifacial Chalcogenide Thin‐Film Solar Cells: Concepts, Challenges, and Opportunities
by: Amanat Ali, et al.
Published: (2025)
by: Amanat Ali, et al.
Published: (2025)
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
by: Li, Jiajia, et al.
Published: (2023)
by: Li, Jiajia, et al.
Published: (2023)
Disentangled Graph Prompting for Out-Of-Distribution Detection
by: Yang, Cheng, et al.
Published: (2026)
by: Yang, Cheng, et al.
Published: (2026)
Data-centric Graph Learning: A Survey
by: Guo, Yuxin, et al.
Published: (2023)
by: Guo, Yuxin, et al.
Published: (2023)
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
by: Li, Xin, et al.
Published: (2024)
by: Li, Xin, et al.
Published: (2024)
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
by: Wang, Bohao, et al.
Published: (2026)
by: Wang, Bohao, et al.
Published: (2026)
Exploring Reliable Spatiotemporal Dependencies for Efficient Visual Tracking
by: Shi, Junze, et al.
Published: (2026)
by: Shi, Junze, et al.
Published: (2026)
Similar Items
-
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
by: Li, Yibo, et al.
Published: (2024) -
Endowing Pre-trained Graph Models with Provable Fairness
by: Zhang, Zhongjian, et al.
Published: (2024) -
Data-centric Federated Graph Learning with Large Language Models
by: Yan, Bo, et al.
Published: (2025) -
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
by: Zhang, Zhongjian, et al.
Published: (2024) -
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
by: Zhang, Zhongjian, et al.
Published: (2026)