Results 41 to 50 of about 1,639,507 (316)
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [PDF]
AAAI Conference on Artificial Intelligence, 2020 Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads.
Mengzhang Li, Zhanxing Zhu
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GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks [PDF]
The Web Conference, 2023 Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation.
Zemin Liu +3 more
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Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View [PDF]
AAAI Conference on Artificial Intelligence, 2019 Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes).
Deli Chen +5 more
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Towards Deeper Graph Neural Networks [PDF]
Knowledge Discovery and Data Mining, 2020 Graph neural networks have shown significant success in the field of graph representation learning. Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations.
Meng Liu, Hongyang Gao, Shuiwang Ji
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Graph-Informed Neural Networks for Regressions on Graph-Structured Data
Mathematics, 2022 In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN).
Stefano Berrone +4 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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, 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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GPT-GNN: Generative Pre-Training of Graph Neural Networks [PDF]
Knowledge Discovery and Data Mining, 2020 Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain.
Ziniu Hu +4 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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EEG-Based Emotion Recognition Using Regularized Graph Neural Networks [PDF]
IEEE Transactions on Affective Computing, 2019 Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels.
Peixiang Zhong, Di Wang, C. Miao
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