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Early detection of Parkinson's disease using a multi area graph convolutional network. [PDF]
Sci RepHuo H +6 more
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Deep graph convolutional network-based multi-omics integration for cancer driver gene identification. [PDF]
Brief BioinformWu Y, Xu J, Li J, Gu J, Shang X, Li X.
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Graph sparsification with graph convolutional networks
International Journal of Data Science and Analytics, 2021Graphs are ubiquitous across the globe and within science and engineering. Some powerful classifiers are proposed to classify nodes in graphs, such as Graph Convolutional Networks (GCNs). However, as graphs are growing in size, node classification on large graphs can be space and time consuming due to using whole graphs.
Jiayu Li 0002 +5 more
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Graph Convolutional Network Hashing
IEEE Transactions on Cybernetics, 2020Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity graph has been extensively studied for large-scale image retrieval. However, most graph-based hashing methods resort to intractable binary quadratic programs, making them unscalable to massive data.
Xiang Zhou, Fumin Shen, Wei Liu
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Robust graph learning with graph convolutional network
Information Processing and Management, 2022Yingying Wan +3 more
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Graph Convolutional Kernel Machine versus Graph Convolutional Networks
Advances in Neural Information Processing Systems 36, 2023Zhihao Wu 0002 +2 more
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Fuzzy Graph Subspace Convolutional Network
IEEE Transactions on Neural Networks and Learning SystemsGraph convolutional networks (GCNs) are a popular approach to learn the feature embedding of graph-structured data, which has shown to be highly effective as well as efficient in performing node classification in an inductive way. However, with massive nongraph-organized data existing in application scenarios nowadays, it is critical to exploit the ...
Jianhang Zhou +3 more
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Generic Dynamic Graph Convolutional Network for traffic flow forecasting
Information Fusion, 2023Yi Xu, Liangzhe Han, Leilei Sun
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Learnable graph convolutional network and feature fusion for multi-view learning
Information Fusion, 2023Zhaoliang Chen +2 more
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