Possible advisorsa guess from early papers, not confirmed

• Yuan Fang

  Possible advisor · last author on 4 of their early first-author papers, 2024–2025

  Suggested from co-authorship

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• A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning

  Xingtong Yu, Yuan Fang, Zemin Liu, Yuxia Wu, Zhihao Wen, Jianyuan Bo, Xinming Zhang, Steven C. H. Hoi

  arXiv · 2024

  This survey systematically categorizes existing studies into three major families: meta-learning approaches, pre-training approaches, and hybrid approaches, with a finer-grained classification in each family to aid readers in their method selection process.

  23
• Quantizing Text-attributed Graphs for Semantic-Structural Integration

  Jianyuan Bo, Hao Wu, Yuan Fang

  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · 2025

  STAG is proposed, a novel self-supervised framework that directly quantizes graph structural information into discrete tokens using a frozen codebook, offering an elegant solution to bridging graph learning with LLMs.

  5
• Query-Centric Graph Retrieval Augmented Generation

  Yaxiong Wu, Jianyuan Bo, Yongyue Zhang, Sheng Liang, Yong Liu

  arXiv · 2025

  QC-RAG is introduced, a query-centric graph RAG framework that enables query-granular indexing and multi-hop chunk retrieval, and consistently outperforms prior chunk-based and graph-based RAG methods in question answering accuracy, establishing a new paradigm for multi-hop reasoning.

  1
• CORE: Contrastive Masked Feature Reconstruction on Graphs

  Jianyuan Bo, Yuan Fang

  arXiv · 2025

  This research presents Contrastive Masked Feature Reconstruction (CORE), a novel graph self-supervised learning framework that integrates contrastive learning into MFR, and proposes a novel theoretical insight: under specific conditions, the objectives of MFR and node-level GCL converge, despite their distinct operational mechanisms.

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• Contrastive General Graph Matching with Adaptive Augmentation Sampling

  Jianyuan Bo, Yuan Fang

  Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence · 2024

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