33 citations · 71 across the 13 of their papers we have counts for
13 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…
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.…
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…
Hypergraph Foundation Model
Yue Gao, Yifan Feng, Shiquan Liu +4
Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices throu…
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.…
Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation
Yifan Feng, Jiangang Huang, Shaoyi Du +6
We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOL…
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…
Hypergraph Isomorphism Computation
Yifan Feng, Jiashu Han, Shihui Ying +1
The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information. In terms of extracting low-orde…
Deep Hypergraph Structure Learning
Zizhao Zhang, Yifan Feng, Shihui Ying +1
Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-bas…
PVRNet: Point-View Relation Neural Network for 3D Shape Recognition
Haoxuan You, Yifan Feng, Xibin Zhao +3
Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D featu…
MeshNet: Mesh Neural Network for 3D Shape Representation
Yutong Feng, Yifan Feng, Haoxuan You +2
Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation…
Hypergraph Neural Networks
Yifan Feng, Haoxuan You, Zizhao Zhang +2
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Co…
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
Haoxuan You, Yifan Feng, Rongrong Ji +1
3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with…