5 papers · 1 filter
ReCoG: Relative Node Contexts for Multi-Domain Graph Pre-training
Yupeng Tong, Lianze Shan, Jitao Zhao +1
The success of foundation models in language and vision has motivated Graph Foundation Models (GFMs), which learn transferable knowledge through multi-domain graph pre-training. Ex…
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training
Yupeng Tong, Lianze Shan, Jitao Zhao +2
Inspired by the great success of foundation models in the development of artificial general intelligence, Graph Foundation Models (GFMs) have recently attracted significant attenti…
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
Lianze Shan, Ningchong Wang, Jitao Zhao +2
Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream…
LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training
Lianze Shan, Jitao Zhao, Dongxiao He +3
Recent advances in generic large models, such as GPT and DeepSeek, have motivated the introduction of universality to graph pre-training, aiming to learn rich and generalizable kno…
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
Lianze Shan, Jitao Zhao, Dongxiao He +3
Universal graph pre-training has emerged as a key paradigm in graph representation learning, offering a promising way to train encoders to learn transferable representations from u…