Results 51 to 60 of about 9,987 (195)

Hypergraphs with arbitrarily small codegree Turán density

Bulletin of the London Mathematical Society, Volume 58, Issue 4, April 2026.
Abstract The codegree Turán density γ(F)$\gamma (F)$ of a k$k$‐graph F$F$ is the smallest γ∈[0,1)$\gamma \in [0,1)$ such that every k$k$‐graph H$H$ with δk−1(H)⩾(γ+o(1))|V(H)|$\delta _{k-1}(H)\geqslant (\gamma +o(1))\vert V(H)\vert$ contains a copy of F$F$. In this work, we show that for every ε>0$\varepsilon >0$, there is a k$k$‐uniform hypergraph F$F$
Simón Piga, Bjarne Schülke
wiley   +1 more source

Hypergraph Theory: Applications in 5G Heterogeneous Ultra-Dense Networks

, 2017
Heterogeneous ultra-dense network (HUDN) can significantly increase the spectral efficiency of cellular networks and cater for the explosive growth of data traffic in the fifth-generation (5G) communications.
Li, Geoffrey Ye, Li, Yonghui, Song, Lingyang, Zhang, Hongliang   +3 more
core   +1 more source

Hypergraph Computation

Engineering
Practical real-world scenarios such as the Internet, social networks, and biological networks present the challenges of data scarcity and complex correlations, which limit the applications of artificial intelligence. The graph structure is a typical tool
Yue Gao, Shuyi Ji, Xiangmin Han, Qionghai Dai   +3 more
doaj   +1 more source

Large language models for bioinformatics

Quantitative Biology, Volume 14, Issue 1, March 2026.
Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
Wei Ruan, Yanjun Lyu, Jing Zhang, Jiazhang Cai, Peng Shu, Yang Ge, Yao Lu, Shang Gao, Yue Wang, Peilong Wang, Lin Zhao, Tao Wang, Yufang Liu, Luyang Fang, Ziyu Liu, Zhengliang Liu, Yiwei Li, Zihao Wu, Junhao Chen, Hanqi Jiang, Yi Pan, Zhenyuan Yang, Jingyuan Chen, Shizhe Liang, Wei Zhang, Terry Ma, Yuan Dou, Jianli Zhang, Xinyu Gong, Qi Gan, Yusong Zou, Zebang Chen, Yuanxin Qian, Shuo Yu, Jin Lu, Kenan Song, Xianqiao Wang, Andrea Sikora, Gang Li, Xiang Li, Quanzheng Li, Yingfeng Wang, Lu Zhang, Yohannes Abate, Lifang He, Wenxuan Zhong, Rongjie Liu, Chao Huang, Wei Liu, Ye Shen, Ping Ma, Hongtu Zhu, Yajun Yan, Dajiang Zhu, Tianming Liu   +54 more
wiley   +1 more source

Hypergraph Learning with Line Expansion

, 2020
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Abdelzaher, Tarek, Wang, Ruijie, Yang, Chaoqi, Yao, Shuochao   +3 more
core  

Bi-View Contrastive Learning with Hypergraph for Enhanced Session-Based Recommendation

Information
Session-based recommendation (SBR) aims to predict a user’s next interests based on their actions in a single visit. Recent methods utilize graph neural networks to study the pairwise relationship of item transfers, yet these often overlook the complex ...
Zijun Wang, Lai Wei
doaj   +1 more source

A Hypergraph Filtering Based Iterative Decoding Approach for Linear Channel Codes

IEEE Access, 2021
Efficient decoding for general linear channel codes has been a long standing problem. Message passing type algorithms can achieve near-optimal performance for channel codes with sparse check matrices and long code-lengths, but they may suffer from ...
Jiaqi He, Ruyuan Qu, Chongbin Xu, Hui Feng, Xin Wang   +4 more
doaj   +1 more source

A comprehensive review of cluster methods for drug–drug interaction network

Quantitative Biology, Volume 14, Issue 1, March 2026.
Abstract The detection of drug–drug interaction (DDI) is crucial to the rational use of drug combinations. Experimentally, DDI detection is time‐consuming and laborious. Currently, researchers have developed a variety of computational methods to predict DDI.
Shuyuan Cao, Guixia Liu, Xiangrun Zhou, Ji Lv   +3 more
wiley   +1 more source

An Incidence Geometry approach to Dictionary Learning [PDF]

, 2015
We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a sparse representation of data points, by learning \emph{dictionary vectors} upon which the data points can be written as sparse linear combinations.
Sitharam, Meera, Tarifi, Mohamad, Wang, Menghan   +2 more
core  

On affine rigidity

, 2013
We define the notion of affine rigidity of a hypergraph and prove a variety of fundamental results for this notion. First, we show that affine rigidity can be determined by the rank of a specific matrix which implies that affine rigidity is a generic ...
Craig Gotsman, Dylan, Ligang Liu, P. Thurston, Steven J. Gortler   +4 more
core   +4 more sources