2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction
International Journal of Digital Multimedia BroadcastingPredicting origin-destination (OD) flow presents a significant challenge in intelligent transportation due to the intricate dynamic correlations between starting points and destinations.
Cheng Fang, Li Wang
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Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation
IEEE AccessHypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that ...
Hongji Shu +4 more
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Learning Directed Knowledge Using Higher-Ordered Neural Networks: Building a Predictive Framework
Applied SciencesMost graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN)
Yousra Moh Ousellam +4 more
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STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis
AerospaceAccurate fault diagnosis in aerospace transmission systems is essential for ensuring equipment reliability and operational safety, especially for aero-engine bearings.
Panfeng Bao +4 more
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Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
, 2022 As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community. However, commonly used frameworks in deep hypergraph learning focus on hypergraphs with edge-independent vertex weights (EIVWs ...
Huang, Junzhou +6 more
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, 2023 HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural ...
Zhao Li (300229) +4 more
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Multivariate Time Series Anomaly Detection Using Directed Hypergraph Neural Networks
Applied Artificial IntelligenceMultivariate time series anomaly detection is a challenging problem because there can be a number of complex relationships between variables in multivariate time series. Although graph neural networks have been shown to be effective in capturing variable-
Tae Wook Ha, Myoung Ho Kim
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K-hop Hypergraph Neural Network: A Comprehensive Aggregation Approach
The powerful capability of HyperGraph Neural Networks (HGNNs) in modeling intricate, high-order relationships among multiple data samples stems primarily from their ability to aggregate both the direct neighborhood features of individual nodes and those ...
Liu, Jie +4 more
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Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
, 2023 Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data.
Plachouras, Vassilis +3 more
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Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
, 2023 The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs). Capturing the semantics from such networks requires approaches capable of utilizing the full richness of the HINs. Existing methods for modeling
Rudinac, Stevan +6 more
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