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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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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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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 +2 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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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 +4 more
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Structural reinforcement-based graph convolutional networks
Connection Science, 2022 Graph Convolutional Network (GCN) is a tool for feature extraction, learning, and inference on graph data, widely applied in numerous scenarios. Despite the great success of GCN, it performs weakly under some application conditions, such as a multiple ...
Jisheng Qin, Qianqian Wang, Tao Tao
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode 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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Two-way Feature Augmentation Graph Convolution Networks Algorithm [PDF]
Jisuanji kexueGraph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a ...
LI Mengxi, GAO Xindan, LI Xue
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