Results 31 to 40 of about 24,295 (253)
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
Frontiers in Neuroscience, 2020 Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data.
Byung-Hoon Kim, Jong Chul Ye
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Alzheimer’s disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder.
Dominik Klepl +4 more
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GNN-Ensemble: Towards Random Decision Graph Neural Networks
2023 IEEE International Conference on Big Data (BigData), 2023 Graph Neural Networks (GNNs) have enjoyed wide spread applications in graph-structured data. However, existing graph based applications commonly lack annotated data. GNNs are required to learn latent patterns from a limited amount of training data to perform inferences on a vast amount of test data. The increased complexity of GNNs, as well as a single
Wenqi Wei 0001, Mu Qiao, Divyesh Jadav
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Sensors, 2022 Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance.
Yonghong Yu +3 more
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Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-structured inductive bias: the WL-subtree kernel formulation bounds the representational capacity of GNNs, and polynomial-time GNNs are provably incapable of recognizing triangles
Dylan Sandfelder +2 more
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Bilinear Graph Neural Network with Neighbor Interactions
, 2020 Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the ...
Feng, Fuli +6 more
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Continuously Evolution Streaming Graph Neural Network [PDF]
Jisuanji kexueStreaming graphs are widely used in practical applications,and their node and structure characteristics change dynamically with time.Although Graph Neural Network(GNN) is excellent in static graph node classification,it is difficult to apply it directly ...
GUO Husheng, ZHANG Xufei, SUN Yujie, WANG Wenjian
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Auto-GNN: Neural Architecture Search of Graph Neural Networks
CoRR, 2019 Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials are usually required to identify a suitable GNN architecture. It is because the performance of a GNN architecture is significantly affected by the choice of graph convolution ...
Kaixiong Zhou +3 more
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Remote Sensing, 2020 As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements
Yansheng Li +4 more
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Atomistic Line Graph Neural Network for improved materials property predictions
npj Computational Materials, 2021 Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.
Kamal Choudhary, Brian DeCost
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