Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
Fuente:
arXiv
Guardado en:
| Autores principales: | Liang, Chundong, Huang, Yongqi, He, Dongxiao, Li, Peiyuan, Li, Yawen, Jin, Di, Zhang, Weixiong |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
por: Li, Peiyuan, et al.
Publicado: (2026)
por: Li, Peiyuan, et al.
Publicado: (2026)
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
por: He, Dongxiao, et al.
Publicado: (2026)
por: He, Dongxiao, et al.
Publicado: (2026)
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
por: Shan, Lianze, et al.
Publicado: (2026)
por: Shan, Lianze, et al.
Publicado: (2026)
LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training
por: Shan, Lianze, et al.
Publicado: (2026)
por: Shan, Lianze, et al.
Publicado: (2026)
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
por: Huang, Yongqi, et al.
Publicado: (2025)
por: Huang, Yongqi, et al.
Publicado: (2025)
RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
por: Wu, Renzhi, et al.
Publicado: (2025)
por: Wu, Renzhi, et al.
Publicado: (2025)
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling
por: Huang, Yongqi, et al.
Publicado: (2025)
por: Huang, Yongqi, et al.
Publicado: (2025)
Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
por: He, Dongxiao, et al.
Publicado: (2025)
por: He, Dongxiao, et al.
Publicado: (2025)
UniGTE: Unified Graph-Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
por: Wang, Duo, et al.
Publicado: (2025)
por: Wang, Duo, et al.
Publicado: (2025)
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
por: Zhu, Yun, et al.
Publicado: (2023)
por: Zhu, Yun, et al.
Publicado: (2023)
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
por: Liu, Yunhui, et al.
Publicado: (2024)
por: Liu, Yunhui, et al.
Publicado: (2024)
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
por: Sun, Li, et al.
Publicado: (2026)
por: Sun, Li, et al.
Publicado: (2026)
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
por: He, Yufei, et al.
Publicado: (2024)
por: He, Yufei, et al.
Publicado: (2024)
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
por: Zheng, Ziyu, et al.
Publicado: (2026)
por: Zheng, Ziyu, et al.
Publicado: (2026)
Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphs
por: Sun, Shengyin, et al.
Publicado: (2025)
por: Sun, Shengyin, et al.
Publicado: (2025)
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
por: Chitnis, Soham Rohit, et al.
Publicado: (2023)
por: Chitnis, Soham Rohit, et al.
Publicado: (2023)
GraphGPT: Generative Pre-trained Graph Eulerian Transformer
por: Zhao, Qifang, et al.
Publicado: (2023)
por: Zhao, Qifang, et al.
Publicado: (2023)
A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
por: Chen, Haibo, et al.
Publicado: (2026)
por: Chen, Haibo, et al.
Publicado: (2026)
Graph Generative Pre-trained Transformer
por: Chen, Xiaohui, et al.
Publicado: (2025)
por: Chen, Xiaohui, et al.
Publicado: (2025)
Graph Augmentation for Cross Graph Domain Generalization
por: Chen, Guanzi, et al.
Publicado: (2025)
por: Chen, Guanzi, et al.
Publicado: (2025)
Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training
por: He, Yufei, et al.
Publicado: (2024)
por: He, Yufei, et al.
Publicado: (2024)
Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning
por: Du, Enjun, et al.
Publicado: (2025)
por: Du, Enjun, et al.
Publicado: (2025)
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
por: Zhou, Wei, et al.
Publicado: (2026)
por: Zhou, Wei, et al.
Publicado: (2026)
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
por: Davies, Alex O., et al.
Publicado: (2023)
por: Davies, Alex O., et al.
Publicado: (2023)
HDEE: Heterogeneous Domain Expert Ensemble
por: Ersoy, Oğuzhan, et al.
Publicado: (2025)
por: Ersoy, Oğuzhan, et al.
Publicado: (2025)
Towards Pre-trained Graph Condensation via Optimal Transport
por: Yan, Yeyu, et al.
Publicado: (2025)
por: Yan, Yeyu, et al.
Publicado: (2025)
One Router to Route Them All: Homogeneous Expert Routing for Heterogeneous Graph Transformers
por: Shakirov, Georgiy, et al.
Publicado: (2025)
por: Shakirov, Georgiy, et al.
Publicado: (2025)
Graph Embedding in the Graph Fractional Fourier Transform Domain
por: Sheng, Changjie, et al.
Publicado: (2025)
por: Sheng, Changjie, et al.
Publicado: (2025)
GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
por: Zhang, Heming, et al.
Publicado: (2024)
por: Zhang, Heming, et al.
Publicado: (2024)
Attention-Driven Metapath Encoding in Heterogeneous Graphs
por: Katyal, Calder
Publicado: (2024)
por: Katyal, Calder
Publicado: (2024)
GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
por: Guo, Zihao, et al.
Publicado: (2025)
por: Guo, Zihao, et al.
Publicado: (2025)
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
por: Wang, Zhili, et al.
Publicado: (2023)
por: Wang, Zhili, et al.
Publicado: (2023)
CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
por: Chen, Xuanze, et al.
Publicado: (2026)
por: Chen, Xuanze, et al.
Publicado: (2026)
GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs
por: Du, Enjun, et al.
Publicado: (2025)
por: Du, Enjun, et al.
Publicado: (2025)
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
por: Yu, Xingtong, et al.
Publicado: (2024)
por: Yu, Xingtong, et al.
Publicado: (2024)
Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization
por: Hu, Shengchao, et al.
Publicado: (2024)
por: Hu, Shengchao, et al.
Publicado: (2024)
Variational Graph Generator for Multi-View Graph Clustering
por: Chen, Jianpeng, et al.
Publicado: (2022)
por: Chen, Jianpeng, et al.
Publicado: (2022)
Adaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specialization
por: Chu, Yunlong, et al.
Publicado: (2025)
por: Chu, Yunlong, et al.
Publicado: (2025)
Echoless Label-Based Pre-computation for Memory-Efficient Heterogeneous Graph Learning
por: Hu, Jun, et al.
Publicado: (2025)
por: Hu, Jun, et al.
Publicado: (2025)
Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance
por: Di Pasquale, Ricardo, et al.
Publicado: (2024)
por: Di Pasquale, Ricardo, et al.
Publicado: (2024)
Ejemplares similares
-
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
por: Li, Peiyuan, et al.
Publicado: (2026) -
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
por: He, Dongxiao, et al.
Publicado: (2026) -
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
por: Shan, Lianze, et al.
Publicado: (2026) -
LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training
por: Shan, Lianze, et al.
Publicado: (2026) -
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
por: Huang, Yongqi, et al.
Publicado: (2025)