4 citations · 4 across the 5 of their papers we have counts for
5 papers
CORE: Contrastive Masked Feature Reconstruction on Graphs
Jianyuan Bo, Yuan Fang
In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked f…
Query-Centric Graph Retrieval Augmented Generation
Yaxiong Wu, Jianyuan Bo, Yongyue Zhang +2
Graph-based retrieval-augmented generation (RAG) enriches large language models (LLMs) with external knowledge for long-context understanding and multi-hop reasoning, but existing…
Quantizing Text-attributed Graphs for Semantic-Structural Integration
Jianyuan Bo, Hao Wu, Yuan Fang
Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains. With the rise of large language models (LLMs), th…
Contrastive General Graph Matching with Adaptive Augmentation Sampling
Jianyuan Bo, Yuan Fang
Graph matching has important applications in pattern recognition and beyond. Current approaches predominantly adopt supervised learning, demanding extensive labeled data which can…
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
Xingtong Yu, Yuan Fang, Zemin Liu +5
Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements. Earlier techniques often operate in an end-to-end setting, which heavily r…