Results 31 to 40 of about 1,157 (178)

Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution

IEEE Access, 2023
In the field of session-based recommendation by anonymous sessions, the commonly used supervised learning modeling method has the problem of sub-optimal recommendation.
Shunpan Liang, Guozheng Zhang, Wenhui Ren   +2 more
doaj   +1 more source

Molecular hypergraph neural networks

The Journal of Chemical Physics
Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as multi-center bonds and conjugated structures.
Junwu Chen, Philippe Schwaller
openaire   +3 more sources

Node Classification Method Based on Hierarchical Hypergraph Neural Network

Sensors
Hypergraph neural networks have gained widespread attention due to their effectiveness in handling graph-structured data with complex relationships and multi-dimensional interactions.
Feng Xu, Wanyue Xiong, Zizhu Fan, Licheng Sun   +3 more
doaj   +1 more source

Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System

IEEE Access, 2022
Recently, online social networks have enriched the users’ lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in.
Yu Wang, Qilong Zhao
doaj   +1 more source

Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction

Advanced Science, EarlyView.
We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen, Guancen Lin, Chuan‐Shen Hu, Yong Wang   +3 more
wiley   +1 more source

Hyper-Ordinal Pattern: Measuring High-Order Connection Relationship in Brain Disease Networks

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Brain hyper-networks as a kind of hypergraph for brain network analysis, describing the high-order interactions among brain regions, have been extensively utilized in research on brain diseases such as mild cognitive impairment (MCI) and Alzheimer’
Tianyu Du, Kai Ma, Xibei Yang, Daoqiang Zhang   +3 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

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

Preventing Over-Smoothing for Hypergraph Neural Networks

CoRR, 2022
In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships. However, current neural network approaches designed for hypergraphs are mostly shallow, thus limiting their ability to extract information from high-order neighbors.
Chen, Guanzi, Zhang, Jiying, Xiao, Xi, Li, Yang   +3 more
openaire   +2 more sources

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani, Rüdiger von Eisenhart‐Rothe, Klaus Woertler, Michael T. Hirschmann, Daniel Rueckert, Florian Hinterwimmer   +5 more
wiley   +1 more source