方法对比
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| 图卷积网络 (GCN)× | 图注意力网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2018 |
| 提出者≠ | Kipf, T. N. & Welling, M. | Veličković, P. et al. |
| 类型≠ | Spectral graph neural network (semi-supervised node classification) | Graph neural network (attention-based) |
| 开创性文献≠ | Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France. link ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| 别名≠ | GCN, graph convolutional network, spectral graph convolution, Kipf-Welling GCN | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| 相关≠ | 1 | 4 |
| 摘要≠ | Graph Convolutional Network (GCN) is a foundational deep learning architecture for graph-structured data, introduced by Thomas N. Kipf and Max Welling at ICLR 2017. It extends the convolution operation to irregular graph domains via a first-order spectral approximation, enabling each node to aggregate feature information from its neighbors. The model became the canonical baseline for semi-supervised node classification and sparked the modern graph neural network research agenda. | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). |
| ScholarGate数据集 ↗ |
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