Imagen de Portada

Towards Graph Foundation Models: A Transferability Perspective

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Yuxiang, Fan, Wenqi, Wang, Suhang, Ma, Yao
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
por: Wang, Yuxiang, et al.
Publicado: (2025)

Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling
por: Zhao, Tianxiang, et al.
Publicado: (2024)

Active Learning for Graphs with Noisy Structures
por: Chi, Hongliang, et al.
Publicado: (2024)

Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
por: Zhang, Zhiwei, et al.
Publicado: (2024)

Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
por: Wang, Kai, et al.
Publicado: (2024)

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
por: Ma, Qian, et al.
Publicado: (2024)

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
por: Qian, Xiaowei, et al.
Publicado: (2024)

Boosting Graph Foundation Model from Structural Perspective
por: Cheng, Yao, et al.
Publicado: (2024)

Fast Graph Condensation with Structure-based Neural Tangent Kernel
por: Wang, Lin, et al.
Publicado: (2023)

Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback
por: Xiao, Teng, et al.
Publicado: (2024)

Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspective
por: Zhang, Haoyu, et al.
Publicado: (2025)

When Do Graph Foundation Models Transfer? A Data-Centric Theory
por: Zhu, Jiajun, et al.
Publicado: (2026)

GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
por: Zhu, Yun, et al.
Publicado: (2024)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
por: Zhang, Jiahao, et al.
Publicado: (2026)

Towards Graph Foundation Models for Personalization
por: Damianou, Andreas, et al.
Publicado: (2024)

Transferable Graph Condensation from the Causal Perspective
por: Du, Huaming, et al.
Publicado: (2026)

Graph Defense Diffusion Model
por: He, Xin, et al.
Publicado: (2025)

Simple and Asymmetric Graph Contrastive Learning without Augmentations
por: Xiao, Teng, et al.
Publicado: (2023)

Disambiguated Node Classification with Graph Neural Networks
por: Zhao, Tianxiang, et al.
Publicado: (2024)

PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection
por: Dai, Enyan, et al.
Publicado: (2024)

Graph Unlearning with Efficient Partial Retraining
por: Zhang, Jiahao, et al.
Publicado: (2024)

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
por: Liu, Jingzhe, et al.
Publicado: (2024)

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
por: Frasca, Fabrizio, et al.
Publicado: (2024)

Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
por: Wang, Yilong, et al.
Publicado: (2025)

Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models
por: Wei, Lanning, et al.
Publicado: (2024)

Counterfactual Learning on Graphs: A Survey
por: Guo, Zhimeng, et al.
Publicado: (2023)

GFT: Graph Foundation Model with Transferable Tree Vocabulary
por: Wang, Zehong, et al.
Publicado: (2024)

Griffin: Towards a Graph-Centric Relational Database Foundation Model
por: Wang, Yanbo, et al.
Publicado: (2025)

Position: Graph Foundation Models are Already Here
por: Mao, Haitao, et al.
Publicado: (2024)

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
por: Wang, Zehong, et al.
Publicado: (2024)

Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
por: Li, Quan, et al.
Publicado: (2024)

GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
por: Xu, Haoyan, et al.
Publicado: (2025)

Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
por: Wang, Fali, et al.
Publicado: (2024)

A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
por: Pan, Junjun, et al.
Publicado: (2025)

A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives
por: Lai, Junhong, et al.
Publicado: (2025)

Shape-aware Graph Spectral Learning
por: Xu, Junjie, et al.
Publicado: (2023)

Bridging Input Feature Spaces Towards Graph Foundation Models
por: Eliasof, Moshe, et al.
Publicado: (2026)

Stealing Training Graphs from Graph Neural Networks
por: Lin, Minhua, et al.
Publicado: (2024)

Unlearning Inversion Attacks for Graph Neural Networks
por: Zhang, Jiahao, et al.
Publicado: (2025)

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
por: Wang, Shuo, et al.
Publicado: (2025)