Results 11 to 20 of about 24,295 (253)
GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm
Mathematics, 2022 Under an internet background involving artificial intelligence and big data—unstructured, materialized, network graph-structured data, such as social networks, knowledge graphs, and compound molecules, have gradually entered into various specific ...
Kao Ge, Jian-Qiang Zhao, Yan-Yong Zhao
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Alexandria Engineering JournalBy combining graph neural networks and multiple attention mechanisms, a GNN-MAM (Graph neural network based on multiple attention mechanisms) model was developed, which utilizes the structural characteristics of graph neural networks to capture complex ...
Yuli Ma, MyeongCheol Choi, Yelin Weng
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Text Classification Method Based on Information Fusion of Dual-graph Neural Network [PDF]
Jisuanji kexue, 2022 Graph neural networks are recently applied in text classification tasks.Compared with graph convolution network,the text level graph neural network model based on message passing(MP-GNN) has the advantages of low memory usage and supporting online ...
YAN Jia-dan, JIA Cai-yan
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RAN-GNNs: Breaking the Capacity Limits of Graph Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 Graph neural networks have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i ...
Valsesia D., Fracastoro G., Magli E.
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TF-GNN: Graph Neural Networks in TensorFlow
CoRR, 2022 TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling machine learning researchers and advanced developers, TF-GNN offers low-code solutions to empower the ...
Ferludin, Oleksandr +26 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In this article, we propose a novel deep domain adaptation method based on graph neural network (GNN) for multitemporal hyperspectral remote sensing images. In GNN, graphs are constructed for source and target data, respectively.
Wenjin Wang, Li Ma, Min Chen, Qian Du
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Shared Graph Neural Network for Channel Decoding
Applied Sciences, 2023 With the application of graph neural network (GNN) in the communication physical layer, GNN-based channel decoding algorithms have become a research hotspot.
Qingle Wu +5 more
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Policy-GNN: Aggregation Optimization for Graph Neural Networks [PDF]
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020 Accepted by ACM SIGKDD'20 research ...
Kwei-Herng Lai +3 more
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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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From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics
IEEE Access, 2023 Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graph-structured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five ...
Yuzhuo Li +3 more
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