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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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Aspect-level Sentiment Analysis Integrating Syntactic Distance and Aspect-attention [PDF]
Jisuanji kexue, 2023 Currently,the over-smoothing problem arises from deep convolution in syntactic dependency tree-based graph convolutional networks.This problem prevents the convolutional graph network from extracting the global node information of the syntactic ...
ZHANG Longji, ZHAO Hui
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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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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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Graph convolutional network for fMRI analysis based on connectivity neighborhood
Network Neuroscience, 2021 There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the ...
Lebo Wang, Kaiming Li, Xiaoping P. Hu
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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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Hybrid Graph Models for Traffic Prediction
Applied Sciences, 2023 Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions.
Renyi Chen, Huaxiong Yao
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Multi-Semantic Alignment Graph Convolutional Network
Connection Science, 2022 Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce.
Jisheng Qin +3 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 +4 more
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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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