Results 81 to 90 of about 5,998 (189)

NE-DCHL: Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning for Next Point-of-Interest Recommendation

Information
Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs)
Hongwei Zhang, Guolong Wang, Xiaofeng Yan   +2 more
doaj   +1 more source

Toward Useful Quantum Kernels

Advanced Quantum Technologies, Volume 8, Issue 12, December 2025.
The hybrid approach to Quantum Supervised Machine Learning is compatible with Noisy Intermediate Scale Quantum (NISQ) devices but hardly useful. Pure quantum kernels requiring fault‐tolerant quantum computers are more promising. Examples are kernels computed by means of the Quantum Fourier Transform (QFT) and kernels defined via the calculation of ...
Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro   +2 more
wiley   +1 more source

HyMePre: A Spatial–Temporal Pretraining Framework with Hypergraph Neural Networks for Short-Term Weather Forecasting

Applied Sciences
Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making.
Fei Wang, Dawei Lin, Baojun Chen, Guodong Jing, Yi Geng, Xudong Ge, Daoming Wei, Ning Zhang   +7 more
doaj   +1 more source

Wasserstein Hypergraph Neural Network

The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learning has become mainstream over the past decade, representing higher-order relationships through hypergraphs is rapidly gaining momentum.
Duta, Iulia, Liò, Pietro
openaire   +2 more sources

Hypergraph Neural Networks for Coalition Formation Under Uncertainty

Algorithms
Identifying effective coalitions of agents for task execution within large multiagent settings is a challenging endeavor. The problem is exacerbated by the presence of coalitional value uncertainty, which is due to uncertainty regarding the values of ...
Gerasimos Koresis, Charilaos Akasiadis, Georgios Chalkiadakis   +2 more
doaj   +1 more source

Recent Advances in Hypergraph Neural Networks

Journal of the Operations Research Society of China
Abstract The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing.
Mu-Rong Yang, Xin-Jian Xu
openaire   +3 more sources

2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction

International Journal of Digital Multimedia Broadcasting
Predicting origin-destination (OD) flow presents a significant challenge in intelligent transportation due to the intricate dynamic correlations between starting points and destinations.
Cheng Fang, Li Wang
doaj   +1 more source

Attention-Based Hypergraph Neural Network Personalized Recommendation

Applied Sciences
Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience. To address the limitations of existing studies in multimodal heterogeneous data fusion and high-order relationship modeling, this research proposes
Peihua Xu, Maoyuan Zhang
openaire   +2 more sources

Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation

IEEE Access
Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that ...
Hongji Shu, Chaojun Meng, Pasquale De Meo, Qing Wang, Jia Zhu   +4 more
doaj   +1 more source

Learning Directed Knowledge Using Higher-Ordered Neural Networks: Building a Predictive Framework

Applied Sciences
Most graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN)
Yousra Moh Ousellam, Bikram Pratim Bhuyan, Rachida Fissoune, Galina Ivanova, Amar Ramdane-Cherif   +4 more
doaj   +1 more source