Results 51 to 60 of about 1,404 (207)

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

A Dynamic Correlation‐Information‐Fusion‐Based Spatiotemporal Network for Traffic Flow Forecasting

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Traffic Flow Forecasting (TFF) is a foundational task in the development of Intelligent Transport Systems (ITSs). The primary challenge is to undertake a comprehensive exploration of the intrinsic dynamic spatiotemporal correlations of the road network, unveiling the long‐term evolutionary traffic trends.
Dawen Xia, Zhan Lin, Xingyan Wang, Ruixi Huang, Jinhui Hu, Yang Hu, Huaqing Li   +6 more
wiley   +1 more source

Hypergraph Neural Networks Based on Enclosing Subgraph Extraction for Temporal Link Prediction

IEEE Access
The Graph Neural Network (GNN) methods based on enclosing subgraph extraction have achieved excellent results in static graph link prediction tasks. However, most real-world networks are dynamic and evolve over time; the traditional models cannot capture
Ying Zhao, Xiangxu Meng, Liang Chen, Junjun Chen, Atul Sajjanhar   +4 more
doaj   +1 more source

GBT‐SAM: A Parameter‐Efficient Depth‐Aware Model for Generalizable Brain Tumor Segmentation on mp‐MRI

International Journal of Imaging Systems and Technology, Volume 36, Issue 4, July 2026.
ABSTRACT Gliomas are aggressive brain tumors that require accurate imaging‐based diagnosis, where automated segmentation plays a central role in assessing tumor morphology and guiding treatment decisions. Manual delineation of gliomas is time‐consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate ...
Cecilia Diana‐Albelda, Roberto Alcover‐Couso, Álvaro García‐Martín, Jesus Bescos, Marcos Escudero‐Viñolo   +4 more
wiley   +1 more source

Hypergraph convolution and hypergraph attention

, 2020
Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest.
Zhang, F, Bai, Song, Torr, PHS
core   +1 more source

DGHSA: derivative graph-based hypergraph structure attack

Scientific Reports
Hypergraph Neural Networks (HGNNs) have been significantly successful in higher-order tasks. However, recent study have shown that they are also vulnerable to adversarial attacks like Graph Neural Networks. Attackers fool HGNNs by modifying node links in
Yang Chen, Zhonglin Ye, Zhaoyang Wang, Jingjing Lin, Haixing Zhao   +4 more
doaj   +1 more source

Implicit Hypergraph Neural Network

2025 IEEE International Conference on Big Data (BigData)
Accepted at IEEE BigData ...
Akash Choudhuri, Yongjian Zhong, Bijaya Adhikari   +2 more
openaire   +2 more sources

CF‐SBERTHet: Collaborative and Textual Knowledge Enhanced Semantic Graphs for Sparse Recommendations

Expert Systems, Volume 43, Issue 7, July 2026.
ABSTRACT Modern e‐commerce platforms face a critical challenge: delivering accurate recommendations under extreme user–item interaction sparsity, where textual context remains systematically underutilised. Existing collaborative filtering methods degrade sharply in sparse settings, while semantic approaches fail to capture collaborative patterns ...
He Ma, Maryam Khanian Najafabadi, Qiyang Wu, WeiPing Kong, Huajun Jie, Fanye Sun, Xiaoyue Bai, Mohammadhossein Ahmadi   +7 more
wiley   +1 more source

Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations

Applied Sciences, 2023
Emotion recognition in multi-party conversations (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Recently, many approaches have been devoted to exploiting inter-dependency and self-dependency among ...
Haojie Xu, Cheng Zheng, Zhuoer Zhao, Xiao Sun   +3 more
doaj   +1 more source

Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration

Human Brain Mapping, Volume 47, Issue 8, June 1, 2026.
We propose MSSM+, an extension of multiscale structural mapping (MSSM), together with surface supervertex mapping (SSVM) and a Supervertex Vision Transformer (SV‐ViT). Together, these methods exhibited better performance in detecting Alzheimer's disease and less variability across MR vendors than MSSM.
Geonwoo Baek, David H. Salat, Ikbeom Jang, for the Alzheimer's Disease Neuroimaging Initiative   +3 more
wiley   +1 more source