Results 81 to 90 of about 1,216 (173)

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions. [PDF]

Bioinformatics, 2022
Nguyen DA, Nguyen CH, Petschner P, Mamitsuka H.   +3 more
europepmc   +1 more source

DPHGNN: A Dual Perspective Hypergraph Neural Networks

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Accepted in SIGKDD'24 -- Research ...
Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee, Tanmoy Chakraborty 0002   +4 more
openaire   +2 more sources

A novel Mamba-hypergraph enhanced time-frequency fusion network for multivariate time series classification

Complex & Intelligent Systems
Multivariate time series (MTS) classification is a crucial research area with broad applications in action recognition, healthcare, and system monitoring.
Jianjian Jiang, Chuxin Zhuang, Fangyan Lei, Ziwei Chen, Lintao Xiao, Minjing Liang, Jianfeng Peng   +6 more
doaj   +1 more source

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

Multimodal and Temporal Graph Fusion Framework for Advanced Phishing Website Detection

IEEE Access
Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques.
S. Kavya, D. Sumathi
doaj   +1 more source

Hyperedge Anomaly Detection with Hypergraph Neural Network

CoRR
Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications.
Md. Tanvir Alam, Chowdhury Farhan Ahmed, Carson K. Leung   +2 more
openaire   +2 more sources

Hypergraph Neural Networks Accelerate MUS Enumeration

CoRR
Enumerating Minimal Unsatisfiable Subsets (MUSes) is a fundamental task in constraint satisfaction problems (CSPs). Its major challenge is the exponential growth of the search space, which becomes particularly severe when satisfiability checks are expensive.
Hiroya Ijima, Koichiro Yawata
openaire   +2 more sources

Exploring High-Order Skeleton Correlations with Physical and Non-Physical Connection for Action Recognition

Applied Sciences
Hypergraphs have received widespread attention in modeling complex data correlations due to their superior performance. In recent years, some researchers have used hypergraph structures to characterize complex non-pairwise joints in the human skeleton ...
Cheng Wang, Nan Ma, Zhixuan Wu
doaj   +1 more source

Hyperedge Interaction-aware Hypergraph Neural Network

CoRR
Hypergraphs provide an effective modeling approach for modeling high-order relationships in many real-world datasets. To capture such complex relationships, several hypergraph neural networks have been proposed for learning hypergraph structure, which propagate information from nodes to hyperedges and then from hyperedges back to nodes.
Xiaobing Pei, Rongping Ye, Haoran Yang, Ruiqi Wang   +3 more
openaire   +2 more sources

APT attack threat-hunting network model based on hypergraph Transformer

Tongxin xuebao
To solve the problem that advanced persistent threat (APT) in the Internet of things (IoT) environment had the characteristics of strong concealment, long duration, and fast update iterations, it was difficult for traditional passive detection models to ...
Yuancheng LI, Yukun LIN
doaj   +2 more sources