3 citations · 4 across the 8 of their papers we have counts for
8 papers
Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling
Yifan Feng, Guanjie Cheng, Shihui Ying +2
Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (three-dimensional geometry). W…
Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning
Xinyan Zhu, Yaoqi Liu, Yue Gao +3
Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support. With the rapid developmen…
Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation
Rizhuo Huang, Yifan Feng, Rundong Xue +5
Knowledge hypergraphs surpass traditional binary knowledge graphs by encapsulating complex n-ary atomic facts, providing a more comprehensive paradigm for semantic representation.…
Cog-RAG: Cognitive-Inspired Dual-Hypergraph with Theme Alignment Retrieval-Augmented Generation
Hao Hu, Yifan Feng, Ruoxue Li +5
Retrieval-Augmented Generation (RAG) enhances the response quality and domain-specific performance of large language models (LLMs) by incorporating external knowledge to combat hal…
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation
Yifan Feng, Hao Hu, Xingliang Hou +5
Large language models (LLMs) have transformed various sectors, including education, finance, and medicine, by enhancing content generation and decision-making processes. However, t…
Multi-view Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation
Murong Yang, Shihui Ying, Xin-Jian Xu +1
Graph-based multi-view spectral clustering methods have achieved notable progress recently, yet they often fall short in either oversimplifying pairwise relationships or struggling…
Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?
Yifan Feng, Chengwu Yang, Xingliang Hou +4
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data.…
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for Faster Inference
Yifan Feng, Yihe Luo, Shihui Ying +1
Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling. Howeve…