Machine Learning and Knowledge Extraction, 2020 This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption ...
Neda H. Bidoki +2 more
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Bayesian graph convolutional network with partial observations.
PLoS ONEAs a widely studied model in the machine learning and data processing society, graph convolutional network reveals its advantage in non-grid data processing.
Shuhui Luo, Peilan Liu, Xulun Ye
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A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction [PDF]
, 2021 We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data.
Yejian, Wu +3 more
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Accurately Extending the Organic-Trained RexGen Graph Convolutional Neural Network to Inorganic Reaction Prediction [PDF]
, 2023 Computational chemists have taken great interest in machine learning in recent years, as techniques are being developed to produce faster predictions with higher accuracy. In 2019, Coley, et al, proposed a graph convolutional neural network (GCNN) model
Robert, Lavroff, Bobby, Judd
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Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification [PDF]
, 2022 A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG ...
Ji, Xiaopeng, Wen, Peng, Li, Yan
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Tangent Graph Convolutional Network [PDF]
, 2021 Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors.
Luca Pasa +2 more
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Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network
, 2021 In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to ...
Wan, Sheng +5 more
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Unsupervised Domain Adaptive Graph Convolutional Networks [PDF]
, 2020 Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges ...
Zhou, C +14 more
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Structure-aware protein-protein interaction site prediction using deep graph convolutional network
, 2021 MOTIVATION: Protein-protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site
Zhou, Yaoqi +4 more
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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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