G-KAN: Graph Kolmogorov-Arnold Network for Node Classification Using Contrastive Learning
Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. The selection of activation functions and message-passing strategies significantly influences performance. A novel fully connected model, named G-...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11025810/ |
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