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PGSFormer: traffic flow prediction based on joint optimization of progressive graph convolutional networks with subseries transformer [PDF]

Scientific Reports
Traffic flow prediction is challenging due to its complex spatio-temporal correlations and the graph-structured nature of traffic networks. Adaptive graph construction methods have gained attention for their superiority over static graph models. However,
Linlong Chen
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Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhuoran Li, Zhuoran Li, Zhuoran Li, Zhuoran Li, Gege Li, Gege Li, Gege Li, Gege Li, Haixing Zhao, Haixing Zhao, Haixing Zhao, Haixing Zhao   +15 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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Progressive Graph Convolutional Networks for Semi-Supervised Node Classification

IEEE Access, 2021
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. Existing methods use a network structure defined by the user based on experimentation with fixed number of layers and neurons ...
Negar Heidari, Alexandros Iosifidis
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Superpixel Image Classification with Graph Convolutional Neural Networks Based on Learnable Positional Embedding

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, Gwang-Hyun Yu, Ju-Hwan Lee, Dang Thanh Vu, Le Hoang Anh, Hyoung-Gook Kim, Jin-Young Kim   +6 more
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Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

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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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, Andrey Berdnikov, Pavel Ustin   +2 more
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Graph convolutional networks fusing motif-structure information

Scientific Reports, 2022
With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data.
Bin Wang, LvHang Cheng, JinFang Sheng, ZhengAng Hou, YaoXing Chang   +4 more
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Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [PDF]

Jisuanji kexue, 2022
With the wide application of graph neural network technology in the field of natural language processing,the research of text classification based on graph neural networks has received more and more attention.Building graph for text is an important ...
ZHANG Hu, BAI Ping
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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