Results 1 to 10 of about 142,397 (310)
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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Graph Convolutional Neural Network [PDF]
Procedings of the British Machine Vision Conference 2016, 2016 The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the image classification.
Xianghua Xie
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Motif-based Convolutional Neural Network on Graphs [PDF]
CoRR, 2017 This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas. We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance, using high-order connection patterns or ...
Aravind Sankar +2 more
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Non-convolutional Graph Neural Networks [PDF]
Advances in Neural Information Processing Systems 37Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation. Here, we design a simple graph learning module entirely free of convolution operators, coined random walk with unifying memory (RUM ...
Yuanqing Wang, Kyunghyun Cho
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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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Image Denoising with Graph-Convolutional Neural Networks [PDF]
2019 IEEE International Conference on Image Processing (ICIP), 2019 Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks can outperform classical model-based techniques, because they can capture ...
Fracastoro, Giulia +2 more
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Representing Born effective charges with equivariant graph convolutional neural networks [PDF]
Scientific ReportsGraph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the ...
Alex Kutana +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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Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network. [PDF]
J Cheminform, 2021 Chen J, Si YW, Un CW, Siu SWI.
europepmc +3 more sources