Results 21 to 30 of about 409,466 (266)

MBHAN: Motif-Based Heterogeneous Graph Attention Network

Applied Sciences, 2022
Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu, Weiping Lin, Minli Tang, Jiatao Jiang   +3 more
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Graph Rewriting for Graph Neural Networks

, 2023
Originally submitted to ICGT 2023, part of STAF ...
Machowczyk, Adam, Heckel, Reiko
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Review of Graph Neural Networks [PDF]

Jisuanji kexue
With the rapid development of artificial intelligence,deep learning has achieved great success in data that can be represented in Euclidean spaces,such as images,text,and speech.However,it has been difficult to apply deep learning to non-Eucli-dean ...
HOU Lei, LIU Jinhuan, YU Xu, DU Junwei
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Non-Local Graph Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
8 pages, 2 figures, accepted by ...
Meng Liu, Zhengyang Wang, Shuiwang Ji
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Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Data Science and Engineering, 2023
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li, Man-Sheng Chen, Yuefang Gao, Chang-Dong Wang   +3 more
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Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

Frontiers in Artificial Intelligence, 2021
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel.
Paul Y. Wang, Paul Y. Wang, Sandalika Sapra, Sandalika Sapra, Vivek Kurien George, Vivek Kurien George, Gabriel A. Silva, Gabriel A. Silva, Gabriel A. Silva   +8 more
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Benchmarking Graph Neural Networks

, 2020
Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Dwivedi, Vijay Prakash, Joshi, Chaitanya K., Luu, Anh Tuan, Laurent, Thomas, Bengio, Yoshua, Bresson, Xavier   +5 more
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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, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu   +4 more
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Image Denoising with Graph-Convolutional Neural Networks [PDF]

, 2019
Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks can outperform classical model-based techniques, because they can capture ...
Fracastoro, Giulia, Magli, Enrico, Valsesia, Diego   +2 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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