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papers

Publications (13)

cs.LG2026

What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models

Dongxiao He, Siqi Liu, Jitao Zhao +3

cs.AI2026

CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer

Ankang Yang, Jitao Zhao, Di Jin +2

The paper introduces CHARM, a multimodal graph foundation model that uses hierarchical context modeling to enable zero-shot transfer across graph domains without fine-tuning. It en…

#multimodal graphs#graph foundation models#zero-shot transfer#hierarchical context modeling
cs.LG2026

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models

Yi Wang, Jitao Zhao, Di Jin +1

cs.LG2025

Str-GCL: Structural Commonsense Driven Graph Contrastive Learning

Dongxiao He, Yongqi Huang, Jitao Zhao +2

cs.LG2026

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation

Ankang Yang, Jitao Zhao, Dongxiao He +3

cs.LG2026

MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training

Lianze Shan, Jitao Zhao, Dongxiao He +3

cs.LG2026

GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks

Dongxiao He, Wenxuan Sun, Yongqi Huang +2

cs.LG2025

Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling

Yongqi Huang, Jitao Zhao, Dongxiao He +3

cs.LG2026

LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training

Lianze Shan, Jitao Zhao, Dongxiao He +3

cs.LG2026

Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

Lianze Shan, Ningchong Wang, Jitao Zhao +2

cs.LG2025

One Prompt Fits All: Universal Graph Adaptation for Pretrained Models

Yongqi Huang, Jitao Zhao, Dongxiao He +5

cs.LG2026

AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control

Jingbo Cui, Jitao Zhao, Di Jin +1

The paper introduces AgentGFM, a graph foundation model where each node acts as an agent that autonomously decides how to propagate information using a trainable policy, enabling a…

#graph neural networks#foundation models#agent-based learning#information flow control
cs.LG2026

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

Peiyuan Li, Yongqi Huang, Jitao Zhao +3