Results 1 to 10 of about 7,186 (68)
Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification
Defence Technology, 2023 With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult Problem in current research. The graph neural network (GNN) has emerged as an approach to semi-supervised classification, and the application of GNN to ...
Ding Yao +6 more
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Survey of Graph Neural Network [PDF]
Jisuanji gongcheng, 2021 With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
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Survey of Graph Neural Network in Recommendation System [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well.
WU Jing, XIE Hui, JIANG Huowen
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Study on Degree of Node Based Personalized Propagation of Neural Predictions forSocial Networks [PDF]
Jisuanji kexue, 2023 Graph is an important and fundamental data structure that presents in a wide variety of practical scenarios.With the rapid development of the Internet in recent years,there has been a huge increase in social network graph data,and the analysis of this ...
SHAO Yunfei, SONG You, WANG Baohui
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LGNN: a novel linear graph neural network algorithm
Frontiers in Computational Neuroscience, 2023 The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks.
Shujuan Cao +16 more
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RG-GCN: Improved Graph Convolution Neural Network Algorithm Based on Rough Graph
IEEE Access, 2022 The graph convolution neural network uses topological graph to portray inter-node relationships and update node features. However, the traditional topological graph can only describe the certain relationship between nodes (that is, the weight of the ...
Weiping Ding +7 more
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Survey of Knowledge Graph Reasoning Based on Representation Learning [PDF]
Jisuanji kexue, 2023 Knowledge graphs describe objective knowledge in the real world in a structured form,and are confronted with issues of completeness and newly-added knowledge.As an important means of complementing and updating knowledge graphs,know-ledge graph reasoning ...
LI Zhifei, ZHAO Yue, ZHANG Yan
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Power System Network Topology Identification Based on Knowledge Graph and Graph Neural Network
Frontiers in Energy Research, 2021 The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their ...
Changgang Wang, Jun An, Jun An, Gang Mu
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Graph Neural Network Defense Combined with Contrastive Learning [PDF]
Jisuanji kexue yu tansuo, 2023 Although graph neural networks have achieved good performance in the field of graph representation learning, recent studies have shown that graph neural networks are more susceptible to adversarial attacks on graph structure, namely, by adding well ...
CHEN Na, HUANG Jincheng, LI Ping
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Frontiers in Cell and Developmental Biology, 2021 Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight.
Vidya Manian +2 more
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