A gated graph convolutional network with multi-sensor signals for remaining useful life prediction
With the advent of industry 4.0, multi-sensors are utilized to monitor the degradation process
of machinery. When machinery operating, multi-sensor signals have potential relation with …
of machinery. When machinery operating, multi-sensor signals have potential relation with …
MRGAT: multi-relational graph attention network for knowledge graph completion
G Dai, X Wang, X Zou, C Liu, S Cen - Neural Networks, 2022 - Elsevier
One of the most effective ways to solve the problem of knowledge graph completion is
embedding-based models. Graph neural networks (GNNs) are popular and promising …
embedding-based models. Graph neural networks (GNNs) are popular and promising …
Harnessing Heterogeneous Information Networks: A systematic literature review
The integration of multiple heterogeneous data into graph models has been the subject of
extensive research in recent years. Harnessing these resulting Heterogeneous Information …
extensive research in recent years. Harnessing these resulting Heterogeneous Information …
Osgnn: Original graph and subgraph aggregated graph neural network
Abstract Heterogeneous Graph Embedding (HGE) is receiving a great attention from
researchers, as it can be widely and effectively used to solve problems from various real …
researchers, as it can be widely and effectively used to solve problems from various real …
Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification
Graph embedding is an advantageous technique for reducing computational costs and
effectively using graph information in machine learning tasks like classification, clustering …
effectively using graph information in machine learning tasks like classification, clustering …
Node classification oriented Adaptive Multichannel Heterogeneous Graph Neural Network
Y Li, C Jian, G Zang, C Song, X Yuan - Knowledge-Based Systems, 2024 - Elsevier
Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing
node classification on heterogeneous graphs (HGs). These models are built on the …
node classification on heterogeneous graphs (HGs). These models are built on the …
Unsupervised embedding learning for large-scale heterogeneous networks based on metapath graph sampling
H Zhong, M Wang, X Zhang - Entropy, 2023 - mdpi.com
How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous
networks is a key problem in heterogeneous network embedding research. This paper …
networks is a key problem in heterogeneous network embedding research. This paper …
Self-supervised heterogeneous graph learning with iterative similarity distillation
T Wang, Z Pan, G Hu, K Xu, Y Zhang - Knowledge-Based Systems, 2023 - Elsevier
This paper focuses on the self-supervised learning issue on heterogeneous graph.
Currently, contrastive learning has become the dominant approach on various …
Currently, contrastive learning has become the dominant approach on various …
An explainable spatio-temporal graph convolutional network for the biomarkers identification of ADHD
L Chen, Y Yang, A Yu, S Guo, K Ren, Q Liu… - … Signal Processing and …, 2025 - Elsevier
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that lacks a
diagnostic basis based on connection anomalies. Identifying biomarkers associated with …
diagnostic basis based on connection anomalies. Identifying biomarkers associated with …
Node ranking algorithm using Graph Convolutional Networks and mini-batch training
W Li, T Li, E Nikougoftar - Chaos, Solitons & Fractals, 2024 - Elsevier
This paper presents a novel algorithm for ranking nodes in graph-structured data using
Graph Convolutional Networks (GCNs) combined with mini-batch training. The proposed …
Graph Convolutional Networks (GCNs) combined with mini-batch training. The proposed …