Results 31 to 40 of about 1,216 (173)
Hyperbolic Directed Hypergraph-Based Reasoning for Multi-Hop KBQA
Mathematics, 2022 The target of the multi-hop knowledge base question-answering task is to find answers of some factoid questions by reasoning across multiple knowledge triples in the knowledge base.
Guanchen Xiao +4 more
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Investigating Hypernode Classification of Complex Systems Based on High-order Graph Neural Networks
Guidance, Navigation and ControlInvestigating latent interactions beyond direct connections is essential for analyzing complex networks. However, traditional graph structures often fail to capture complex relationships, especially in the high-order interactions among multiple ...
Jiawen Chen +3 more
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Hypergraph Pre-training with Graph Neural Networks
CoRR, 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.
Boxin Du +4 more
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Molecular hypergraph neural networks
The Journal of Chemical PhysicsGraph 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
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Cross-modal Hypergraph Optimisation Learning for Multimodal Sentiment Analysis [PDF]
Jisuanji kexueSentiment expressions are multimodal,and more accurate emotions can be derived through multiple modalities such as verbal,audio,and visual.Studying the interactions among modalities can effectively improve the accuracy of multimodal sentiment analysis ...
JIANG Kun, ZHAO Zhengpeng, PU Yuanyuan, HUANG Jian, GU Jinjing, XU Dan
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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 +3 more
wiley +1 more source
Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph
Scientific ReportsIn recent years, hypergraph neural networks have achieved remarkable success in tasks such as node classification, link prediction, and graph classification, thanks to their powerful computational capabilities.
Libing Bai +4 more
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Multi-Metric Fusion Hypergraph Neural Network for Rotating Machinery Fault Diagnosis
ActuatorsEffective fault diagnosis in rotating machinery means extracting fault features from complex samples. However, traditional data-driven methods often overly rely on labeled samples and struggle with extracting high-order complex features. To address these
Jiaxing Zhu, Junlan Hu, Buyun Sheng
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Implicit Hypergraph Neural Network
2025 IEEE International Conference on Big Data (BigData)Accepted at IEEE BigData ...
Akash Choudhuri +2 more
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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