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Text Classification Based on Attention Gated Graph Neural Network [PDF]
, 2022 To address the problem that the existing text classification work usually ignores the semantic interaction between words when generating text representation,this paper proposes a novel text classification model based on attention gated graph neural ...
DENG Zhao-yang, ZHONG Guo-qiang, WANG Dong
core +2 more sources
PU-GAT: Point cloud upsampling with graph attention network
Graphical Models, 2023 Point cloud upsampling has been extensively studied, however, the existing approaches suffer from the losing of structural information due to neglect of spatial dependencies between points.
Xuan Deng +3 more
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Learnable Graph Convolutional Attention Networks
CoRR, 2022 Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures, respectively, GCNs and GATs.
Adrián Javaloy +3 more
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Graph Adaptive Attention Network with Cross-Entropy
EntropyNon-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes.
Zhao Chen
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Discovering latent node Information by graph attention network
Scientific Reports, 2021 In this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information.
Weiwei Gu +3 more
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Landscape-Enhanced Graph Attention Network for Rumor Detection
, 2021 Landscape-Enhanced Graph Attention Network for Rumor ...
Min Yu (120607) +6 more
core
Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention
IEEE AccessIndustrial Control Systems (ICS) underpin critical infrastructure and face growing cyber–physical threats due to the convergence of operational technology and networked environments. While machine learning–based anomaly detection approaches
Kosti Koistinen +3 more
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Graph Oriented Attention Networks
IEEE AccessGraph Attention Networks (GAT) is a type of neural network architecture designed to effectively model and process data represented as graphs. GATs leverage the concept of attention mechanisms to learn the importance of different nodes in a graph when performing tasks such as node classification or link prediction.
Ouardi Amine, Mohammed Mestari
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Multimodal graph attention network for COVID-19 outcome prediction
Scientific Reports, 2023 When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown.
Matthias Keicher +9 more
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Contextual Graph Attention Network for Aspect-Level Sentiment Classification
Mathematics, 2022 Aspect-level sentiment classification aims to predict the sentiment polarities towards the target aspects given in sentences. To address the issues of insufficient semantic information extraction and high computational complexity of attention mechanisms ...
Yuqing Miao +6 more
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