Saved in:
| Main Authors: | Tang, Ziyuan, Chen, Jie |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Revisiting Random Walks for Learning on Graphs
by: Kim, Jinwoo, et al.
Published: (2024)
by: Kim, Jinwoo, et al.
Published: (2024)
Zero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders
by: Lei, Zhuxin, et al.
Published: (2026)
by: Lei, Zhuxin, et al.
Published: (2026)
Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
by: Wang, Kai, et al.
Published: (2024)
by: Wang, Kai, et al.
Published: (2024)
Toward Understanding BERT-Like Pre-Training for DNA Foundation Models
by: Liang, Chaoqi, et al.
Published: (2023)
by: Liang, Chaoqi, et al.
Published: (2023)
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
by: Davies, Alex O., et al.
Published: (2023)
by: Davies, Alex O., et al.
Published: (2023)
Graph Neural Networks with Feature and Structure Aware Random Walk
by: Zhuo, Wei, et al.
Published: (2021)
by: Zhuo, Wei, et al.
Published: (2021)
Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
by: Hou, Qinhan, et al.
Published: (2026)
by: Hou, Qinhan, et al.
Published: (2026)
Graph Generative Pre-trained Transformer
by: Chen, Xiaohui, et al.
Published: (2025)
by: Chen, Xiaohui, et al.
Published: (2025)
GraphProp: Training the Graph Foundation Models using Graph Properties
by: Sun, Ziheng, et al.
Published: (2025)
by: Sun, Ziheng, et al.
Published: (2025)
SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
by: Tang, Anke, et al.
Published: (2024)
by: Tang, Anke, et al.
Published: (2024)
TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classification
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)
by: Yeh, Chin-Chia Michael, 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)
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
by: Chen, Bo, et al.
Published: (2024)
by: Chen, Bo, et al.
Published: (2024)
Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement
by: Wei, Yuhan, et al.
Published: (2026)
by: Wei, Yuhan, et al.
Published: (2026)
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs
by: Zhao, Huanjing, et al.
Published: (2024)
by: Zhao, Huanjing, et al.
Published: (2024)
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)
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)
Griffin: Towards a Graph-Centric Relational Database Foundation Model
by: Wang, Yanbo, et al.
Published: (2025)
by: Wang, Yanbo, et al.
Published: (2025)
Single Parent Family: A Spectrum of Family Members from a Single Pre-Trained Foundation Model
by: Hajimolahoseini, Habib, et al.
Published: (2024)
by: Hajimolahoseini, Habib, et al.
Published: (2024)
Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
by: Hübotter, Jonas, et al.
Published: (2025)
by: Hübotter, Jonas, et al.
Published: (2025)
OpenGraph: Towards Open Graph Foundation Models
by: Xia, Lianghao, et al.
Published: (2024)
by: Xia, Lianghao, et al.
Published: (2024)
Towards Principled Graph Transformers
by: Müller, Luis, et al.
Published: (2024)
by: Müller, Luis, et al.
Published: (2024)
On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small Models
by: Farhat, Sean, et al.
Published: (2024)
by: Farhat, Sean, et al.
Published: (2024)
Towards a Physics Foundation Model
by: Wiesner, Florian, et al.
Published: (2025)
by: Wiesner, Florian, et al.
Published: (2025)
Foundations and Frontiers of Graph Learning Theory
by: Huang, Yu, et al.
Published: (2024)
by: Huang, Yu, et al.
Published: (2024)
OSF: On Pre-training and Scaling of Sleep Foundation Models
by: Shuai, Zitao, et al.
Published: (2026)
by: Shuai, Zitao, et al.
Published: (2026)
Pre-Trained Model Recommendation for Downstream Fine-tuning
by: Bai, Jiameng, et al.
Published: (2024)
by: Bai, Jiameng, et al.
Published: (2024)
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
by: Ranjan, Rishabh, et al.
Published: (2025)
by: Ranjan, Rishabh, et al.
Published: (2025)
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
by: Wang, Jinbo, et al.
Published: (2025)
by: Wang, Jinbo, et al.
Published: (2025)
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
by: Wei, Xinxu, et al.
Published: (2024)
by: Wei, Xinxu, et al.
Published: (2024)
AnyGraph: Graph Foundation Model in the Wild
by: Xia, Lianghao, et al.
Published: (2024)
by: Xia, Lianghao, et al.
Published: (2024)
From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework
by: Ding, Zihan, et al.
Published: (2026)
by: Ding, Zihan, et al.
Published: (2026)
TrajAware: Graph Cross-Attention and Trajectory-Aware for Generalisable VANETs under Partial Observations
by: Fu, Xiaolu, et al.
Published: (2025)
by: Fu, Xiaolu, et al.
Published: (2025)
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
by: Li, Yanjie, et al.
Published: (2024)
by: Li, Yanjie, et al.
Published: (2024)
Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs
by: El, Batu, et al.
Published: (2025)
by: El, Batu, et al.
Published: (2025)
TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
by: Chen, Hongjiang, et al.
Published: (2026)
by: Chen, Hongjiang, et al.
Published: (2026)
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
by: Zheng, Ziyu, et al.
Published: (2026)
by: Zheng, Ziyu, et al.
Published: (2026)
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph
by: Hao, Shule, et al.
Published: (2025)
by: Hao, Shule, et al.
Published: (2025)
Foundations of Schrödinger Bridges for Generative Modeling
by: Tang, Sophia
Published: (2026)
by: Tang, Sophia
Published: (2026)
A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
by: Vassoyan, Jean, et al.
Published: (2024)
by: Vassoyan, Jean, et al.
Published: (2024)
Similar Items
-
Revisiting Random Walks for Learning on Graphs
by: Kim, Jinwoo, et al.
Published: (2024) -
Zero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders
by: Lei, Zhuxin, et al.
Published: (2026) -
Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
by: Wang, Kai, et al.
Published: (2024) -
Toward Understanding BERT-Like Pre-Training for DNA Foundation Models
by: Liang, Chaoqi, et al.
Published: (2023) -
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
by: Davies, Alex O., et al.
Published: (2023)