Results 71 to 80 of about 5,998 (189)

Unifying Invariant and Variant Knowledge With Dual‐Hypergraph Contrastive Evolution for Temporal Knowledge Graph Reasoning

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Temporal knowledge graph reasoning (TKGR) excels at inferring missing event‐centric facts within a timeline, thereby mitigating the inherent incompleteness of real‐world data. Existing TKGR methods predominantly exploit intrasnapshot structural patterns and intersnapshot temporal dependencies.
Fei Chen, Bing Guo, Zhangtao Cheng, Yan Shen, Mingjie Zhao, Yi Li, Fan Zhou, Richard Murray   +7 more
wiley   +1 more source

Tensorized Hypergraph Neural Networks

Hypergraph neural networks (HGNN) have recently become attractive and received significant attention due to their excellent performance in various domains. However, most existing HGNNs rely on first-order approximations of hypergraph connectivity patterns, which ignores important high-order information.
Wang, Maolin, Zhen, Yaoming, Pan, Yu, Zhao, Yao, Zhuang, Chenyi, Xu, Zenglin, Guo, Ruocheng, Zhao, Xiangyu   +7 more
openaire   +2 more sources

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

Uncovering Regulatory Networks Through Residual Graph Learning of circRNA–miRNA Interactions

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Circular RNAs (circRNAs) and microRNAs (miRNAs) are key regulators of gene expression, and their interactions are involved in many biological processes and diseases. Nevertheless, the experimental validation of circRNA–miRNA interactions (CMIs) remains challenging due to resource and technical constraints; therefore, robust computational models are ...
Murtada K. Elbashir, Madallah Alruwaili, Mahmood Mohamed, Kallol Mohanta   +3 more
wiley   +1 more source

CoRoFR: Community Detection of Feature Graph Improves Feature Selection Using Robust Fuzzy Rough Set

Advanced Intelligent Systems, Volume 7, Issue 12, December 2025.
In machine learning, features often function as communities in many tasks, especially in medicine. However, existing feature selection methods struggle to mine feature collaborations, which can boost predictive performance. Moreover, they are noise‐sensitive, leading to suboptimal feature selection and accuracy degradation.
Duanyang Feng, Wenjian Liu, Sheng Peng, Yuankun Liu, Wanlei Zhou   +4 more
wiley   +1 more source

EasyHypergraph: an open-source software for fast and memory-saving analysis and learning of higher-order networks

Humanities & Social Sciences Communications
Higher-order relationships exist widely across different disciplines. In the realm of real-world systems, significant interactions involving multiple entities are common.
Bodian Ye, Min Gao, Xiu-Xiu Zhan, Xinlei He, Zi-Ke Zhang, Qingyuan Gong, Xin Wang, Yang Chen   +7 more
doaj   +1 more source

PLNet: Persistent Laplacian neural network for protein–protein binding free energy prediction

Protein Science, Volume 34, Issue 12, December 2025.
Abstract Recent advances in topology‐based modeling have greatly improved molecular prediction tasks, particularly in protein–ligand binding affinity. However, when the focus shifts to predicting protein–protein interactions (PPIs) binding free energy, the question becomes significantly more challenging due to the ineffective use of topological ...
Xingjian Xu, Chunmei Wang, Guo‐Wei Wei, Jiahui Chen   +3 more
wiley   +1 more source

Heterogeneous Temporal Hypergraph Neural Network

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex networks, GRL methods designed for complex heterogeneous temporal graphs (HTGs) have been proposed and have achieved successful applications in various fields.
Liu, Huan, Jiao, Pengfei, Gao, Mengzhou, Chen, Chaochao, Jin, Di   +4 more
openaire   +2 more sources

Hypergraph Pre-training with Graph Neural Networks

, 2021
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
Du, Boxin, Yuan, Changhe, Barton, Robert, Neiman, Tal, Tong, Hanghang   +4 more
openaire   +2 more sources

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
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