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PGSFormer: traffic flow prediction based on joint optimization of progressive graph convolutional networks with subseries transformer [PDF]
Scientific ReportsTraffic 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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Node-Feature Convolution for Graph Convolutional Networks [PDF]
Pattern Recognition, 2022 Graph convolutional network (GCN) is an effective neural network model for graph representation learning. However, standard GCN suffers from three main limitations: (1) most real-world graphs have no regular connectivity and node degrees can range from one to hundreds or thousands, (2) neighboring nodes are aggregated with fixed weights, and (3) node ...
Zhang, L., Song, H., Aletras, N., Lu, H.
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Dynamic graph convolutional networks [PDF]
Pattern Recognition, 2020 Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time. Our goal is to jointly exploit structured data and temporal information through the use of a neural network model.
Franco Manessi +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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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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Simple Graph Convolutional Networks
CoRR, 2021 Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing simple graph convolution operators, that can be implemented ...
Luca Pasa +3 more
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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-Revised Convolutional Network [PDF]
, 2021 Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice ...
Donghan Yu +4 more
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Lorentzian Graph Convolutional Networks [PDF]
Proceedings of the Web Conference 2021, 2021 Les réseaux convolutionnels de graphes (GCN) ont récemment fait l'objet d'une attention considérable de la part de la recherche. La plupart des GCN apprennent les représentations de nœuds en géométrie euclidienne, mais cela pourrait avoir une distorsion élevée dans le cas de l'intégration de graphes avec une structure sans échelle ou hiérarchique ...
Yiding Zhang +4 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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