Results 11 to 20 of about 37,604 (258)
Dual-channel deep graph convolutional neural networks [PDF]
Frontiers in Artificial IntelligenceThe dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
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Co-embedding of edges and nodes with deep graph convolutional neural networks [PDF]
Scientific Reports, 2023 Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing
Yuchen Zhou +7 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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Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks [PDF]
BiomimeticsSystematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain.
Mehdi Khaleghi +5 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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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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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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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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Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
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Local Graph Convolutional Networks for Cross-Modal Hashing
, 2021 Cross-modal hashing aims to map the data of different modalities into a common binary space to accelerate the retrieval speed. Recently, deep cross-modal hashing methods have shown promising performance by applying deep neural networks to facilitate ...
Sen Wang +11 more
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