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A deep graph convolutional neural network architecture for graph classification. [PDF]
PLoS ONE, 2023 Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to
Yuchen Zhou +3 more
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AAGCN: a graph convolutional neural network with adaptive feature and topology learning [PDF]
Scientific ReportsIn recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power.
Bin Wang +3 more
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Cells, 2019 Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis.
Ping Xuan +4 more
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PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network [PDF]
BMC BioinformaticsBackground Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods.
J. Ouyang, Y. Gao, Y. Yang
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Multiscale Global Adaptive Attention Graph Neural Network [PDF]
Jisuanji kexue yu tansuo, 2023 Dynamic multiscale graph neural networks have high motion prediction errors due to the low correlation between the internal joints of body parts and the limited perceptual fields.
GOU Ruru, YANG Wenzhu, LUO Zifei, YUAN Yunfeng
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Applied Sciences, 2022 Graph convolutional neural networks (GCNNs) have been successfully applied to a wide range of problems, including low-dimensional Euclidean structural domains representing images, videos, and speech and high-dimensional non-Euclidean domains, such as ...
Ji-Hun Bae +6 more
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SPATIOTEMPORAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR METRO FLOW PREDICTION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Forecasting urban metro flow accurately plays an important role for station management and passenger safety. Owing to the limitations of non-linearity and complexity of traffic flow data, traditional methods cannot satisfy the requirements of effectively
S. Jin, C. Jing, Y. Wang, X. Lv
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Online social network user performance prediction by graph neural networks
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN)
Fail Gafarov +2 more
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Review of Text Classification Methods Based on Graph Convolutional Network [PDF]
Jisuanji kexue, 2022 Text classification is a common task in natural language processing,in which there are a lot of research and progress based on machine learning and deep learning.However,these traditional methods can only process Euclidean spatial data,and cannot express
TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo
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PLoS ONE, 2021 Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding ...
Jeongtae Son, Dongsup Kim
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