Results 11 to 20 of about 437,099 (249)
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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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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Graph Neural Networks in Network Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph.
Alaa Bessadok +2 more
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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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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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SCIENTIA SINICA Mathematica, 2020 Summary: In recent years, with the emergence of massive data, graph structure data that can represent complex relationships between objects has received more and more attention and has brought great challenges to existing algorithms. As a deep topology information can be revealed, graph neural network models have been widely used in many fields such as
Bai, Bo +7 more
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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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Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data
Mathematics, 2023 Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets.
Anna Boronina +2 more
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Intelligent prediction method of network performance based on graph neural network
Dianxin kexue, 2022 There are some problems in the traditional network performance prediction technology, such as incomplete network state acquisition and poor accuracy of network performance evaluation.Combined with the characteristics of graph neural network learning and ...
Yijiang LI +5 more
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, 2023 In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs.
Zhang, Zhen +4 more
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