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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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Interpretable learning based Dynamic Graph Convolutional Networks for Alzheimer’s Disease analysis
Information Fusion, 2022Xiaofeng Zhu, Junbo
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Are Graph Convolutional Networks With Random Weights Feasible?
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Chang-Qin Huang, Ming Li, Feilong Cao
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Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering
IEEE Transactions on Knowledge and Data Engineering, 2023Zonghan Wu, Shirui Pan, Guodong Long
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Semi-supervised classification by graph p-Laplacian convolutional networks
Information Sciences, 2021Fu Sichao, Weifeng Liu, Yicong Zhou
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Exploiting node-feature bipartite graph in graph convolutional networks
Information Sciences, 2023Xin Huang
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Neighborhood convolutional graph neural network
Knowledge-Based Systems, 2023Jinsong Chen 0002 +2 more
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Graph convolutional networks with multi-level coarsening for graph classification
Knowledge-Based Systems, 2020Maoguo Gong, Alex K Qin
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Graph Neural Networks with Convolutional ARMA Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Filippo M Bianchi +2 more
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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks
ACM Transactions on Graphics, 2022Hongbo Fu, Youyi Zheng
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