7 papers
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
Peiyuan Li, Yongqi Huang, Jitao Zhao +3
Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream app…
Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
Chundong Liang, Yongqi Huang, Dongxiao He +4
Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most existing methods are designed f…
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
Dongxiao He, Wenxuan Sun, Yongqi Huang +2
Graph Prompt Learning (GPL) has recently emerged as a promising paradigm for downstream adaptation of pre-trained graph models, mitigating the misalignment between pre-training obj…
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…
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang, Jitao Zhao, Dongxiao He +5
Graph Prompt Learning (GPL) has emerged as a promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment bet…
Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
Dongxiao He, Yongqi Huang, Jitao Zhao +2
Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations…