方法对比
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| 半监督图神经网络× | 标签传播× | |
|---|---|---|
| 领域≠ | 深度学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 (GCN formulation); 2004 (label propagation roots) | 2002 |
| 提出者≠ | Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor) | Zhu, X. & Ghahramani, Z. |
| 类型≠ | Semi-supervised graph representation learning | Graph-based semi-supervised classification |
| 开创性文献≠ | Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR 2017). link ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| 别名 | Semi-supervised GNN, GNN semi-supervised learning, graph-based semi-supervised classification, semi-supervised node classification | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| 相关≠ | 4 | 3 |
| 摘要≠ | A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classification accuracy even when labeled examples are scarce. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
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