3 citations · 4 across the 6 of their papers we have counts for
6 papers
Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
Yifan Jin, Qirui Ji, Bin Qin +4
Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference. However, the param…
HTG-GCL: Leveraging Hierarchical Topological Granularity from Cellular Complexes for Graph Contrastive Learning
Qirui Ji, Bin Qin, Yifan Jin +5
Graph contrastive learning (GCL) aims to learn discriminative semantic invariance by contrasting different views of the same graph that share critical topological patterns. However…
Introducing Diminutive Causal Structure into Graph Representation Learning
Hang Gao, Peng Qiao, Yifan Jin +3
When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable c…
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
Jiangmeng Li, Fei Song, Yifan Jin +4
As a novel and effective fine-tuning paradigm based on large-scale pre-trained language models (PLMs), prompt-tuning aims to reduce the gap between downstream tasks and pre-trainin…
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
Jiangmeng Li, Yifan Jin, Hang Gao +3
Graph contrastive learning (GCL) aims to align the positive features while differentiating the negative features in the latent space by minimizing a pair-wise contrastive loss. As…
Rethinking Causal Relationships Learning in Graph Neural Networks
Hang Gao, Chengyu Yao, Jiangmeng Li +5
Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustne…