方法对比
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| 图注意力网络× | 层次聚类× | |
|---|---|---|
| 领域≠ | 深度学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018 | 1963 |
| 提出者≠ | Veličković, P. et al. | Ward, J. H. |
| 类型≠ | Graph neural network (attention-based) | Unsupervised clustering (agglomerative) |
| 开创性文献≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| 别名≠ | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| 相关 | 4 | 4 |
| 摘要≠ | 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). | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. |
| ScholarGate数据集 ↗ |
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