Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Stohastiskais bloku modelis× | Grafu uzmanības tīkls (Graph Attention Network, GAT)× | |
|---|---|---|
| Nozare≠ | Tīklu analīze | Dziļā mācīšanās |
| Saime≠ | Process / pipeline | Machine learning |
| Izcelsmes gads≠ | 1983 | 2018 |
| Autors≠ | — | Veličković, P. et al. |
| Tips≠ | Probabilistic generative graph model | Graph neural network (attention-based) |
| Pirmavots≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Citi nosaukumi | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Saistītās≠ | 7 | 4 |
| Kopsavilkums≠ | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. | 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). |
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