Results 91 to 100 of about 9,987 (195)

A Static-Dynamic Hypergraph Neural Network Framework Based on Residual Learning for Stock Recommendation

Complexity
Stock ranking prediction is an effective method for achieving a high investment return and plays a crucial role in investment decisions. However, previous studies have overlooked the interconnections among stocks or have solely relied on predefined ...
Jianlong Hao, Zhibin Liu, Qiwei Sun, Chen Zhang, Jie Wang   +4 more
doaj   +1 more source

AHD-SLE: Anomalous Hyperedge Detection on Hypergraph Symmetric Line Expansion

Axioms
Graph anomaly detection aims to identify unusual patterns or structures in graph-structured data. Most existing research focuses on anomalous nodes in ordinary graphs with pairwise relationships.
Yingle Li, Hongtao Yu, Haitao Li, Fei Pan, Shuxin Liu   +4 more
doaj   +1 more source

Random Walks on Hypergraphs with Edge-Dependent Vertex Weights

, 2019
Hypergraphs are used in machine learning to model higher-order relationships in data. While spectral methods for graphs are well-established, spectral theory for hypergraphs remains an active area of research.
Chitra, Uthsav, Raphael, Benjamin J
core  

Hypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs

Hypergraphs, as a generalization of traditional graphs, naturally capture high-order relationships. In recent years, hypergraph neural networks (HNNs) have been widely used to capture complex high-order relationships. However, most existing hypergraph neural network methods inherently rely on the homophily assumption, which often does not hold in real ...
Guan, Renchu, Li, Xuyang, Zhang, Yachao, Pang, Wei, Giunchiglia, Fausto, Li, Ximing, Liu, Yonghao, Feng, Xiaoyue   +7 more
openaire   +2 more sources

CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network [PDF]

IEEE Transactions on Knowledge and Data Engineering
Accepted by ...
Yumeng Song, Yu Gu, Tianyi Li, Jianzhong Qi, Zhenghao Liu, Christian S. Jensen, Ge Yu   +6 more
openaire   +2 more sources

Capturing User Preferences via Multi-Perspective Hypergraphs with Contrastive Learning for Next-Location Prediction

Applied Sciences
With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services.
Fengyu Liu, Kexin Zhang, Chao Lian, Yunong Tian   +3 more
doaj   +1 more source

Robust Financial Fraud Detection via Causal Intervention and Multi-View Contrastive Learning on Dynamic Hypergraphs

Mathematics
Financial fraud detection is critical to modern economic security, yet remains challenging due to collusive group behavior, temporal drift, and severe class imbalance.
Xiong Luo
doaj   +1 more source

Identifying autism spectrum disorder from multi-modal data with privacy-preserving

npj Mental Health Research
The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models.
Haishuai Wang, Hezi Jing, Jianjun Yang, Chao Liu, Liwei Hu, Guangyu Tao, Ziping Zhao, Ning Shen   +7 more
doaj   +1 more source

Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning

Gong-kuang zidonghua
The vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues, leading to frequent false positives in traditional machine learning-based fault diagnosis methods.
LI Xin, LI Shuhua, CHEN Hao, SI Lei, WEI Dong, ZOU Xiaoyu   +5 more
doaj   +1 more source

Hypergraph regularized nonnegative triple decomposition for multiway data analysis

Scientific Reports
Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high.
Qingshui Liao, Qilong Liu, Fatimah Abdul Razak   +2 more
doaj   +1 more source