Results 61 to 70 of about 1,639,507 (316)
Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network
Sensors, 2023 In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang +4 more
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STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting
Mathematics, 2022 Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data.
Yafeng Gu, Li Deng
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Graph Clustering with Graph Neural Networks
, 2020 Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs.
Tsitsulin, Anton +3 more
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Locally Private Graph Neural Networks [PDF]
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021 Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information.
Sajadmanesh, Sina, Gatica-Perez, Daniel
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A Survey on Graph Neural Networks for Microservice-Based Cloud Applications
Sensors, 2022 Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in ...
Hoa Xuan Nguyen +2 more
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Two-Level Graph Neural Network [PDF]
IEEE Transactions on Neural Networks and Learning SystemsGraph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, existing GNNs focus on capturing node-level information and neglect high-level information.
Xing Ai +3 more
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The Laplacian spectrum of neural networks
Frontiers in Computational Neuroscience, 2014 The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks.
Siemon ede Lange +2 more
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IEEE Transactions on Neural Networks and Learning SystemsGraphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations.
Xuexin Chen +5 more
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Graph coloring with physics-inspired graph neural networks
Physical Review Research, 2022 We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multiclass node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model ...
Martin J. A. Schuetz +3 more
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Graph Neural Network: A Comprehensive Review on Non-Euclidean Space
IEEE Access, 2021 This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data.
Nurul A. Asif +11 more
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