4 papers
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…
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…
Contrastive Representation for Interactive Recommendation
Jingyu Li, Zhiyong Feng, Dongxiao He +3
Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. I…